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Statistical Analysis of High Frequency Ultrasonic Backscattered Signals from Basal Cell Carcinomas
Ultrasound in Med. & Biol., Vol. 38, No. 10, pp. 1811–1819, 2012 Copyright Ó 2012 World Federation for Ultrasound in Medicine & Biology Printed in the USA. All rights reserved 0301-5629/$ - see front matter
http://dx.doi.org/10.1016/j.ultrasmedbio.2012.06.001
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Original Contribution STATISTICAL ANALYSIS OF HIGH FREQUENCY ULTRASONIC BACKSCATTERED SIGNALS FROM BASAL CELL CARCINOMAS LORENA ITATI PETRELLA,*y HELIOMAR DE AZEVEDO VALLE,z PAULO ROBERTO ISSA,x ~O CARLOS MACHADO,yjj and WAGNER COELHO A. PEREIRAy CARLOS JOSE MARTINS,x JOA
* Laboratory of Ultrasound, National Institute of Metrology, Quality and Technology, Duque de Caxias, RJ, Brazil; y Biomedical Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil; z Pathologic Anatomy Department, Gaffree and Guinle University Hospital, Rio de Janeiro, RJ, Brazil; x Dermatology Department, Gaffree and Guinle University Hospital, Rio de Janeiro, RJ, Brazil; and jj Post-Graduation Program in Surgical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil (Received 25 October 2011; revised 21 April 2012; in final form 4 June 2012)
Abstract—A statistical approach was implemented in the study of histologic characteristics from ex vivo basal cell carcinomas, based on the properties of backscattered acoustic waves, for the purpose of evaluating the method as a diagnostic tool. The study was developed using an ultrasound biomicroscope working at a frequency of 45 MHz. The parameters examined were signal-to-noise ratio (SNR), and shape parameters from the Weibull (bW) and generalized gamma (cGG and yGG) probability density functions. Twenty-seven carcinomatous skin samples were obtained from volunteer patients and classified into two groups (BCC1 and BCC2) based on the distribution patterns of their tumor nests; also, seven non-tumoral samples were used for comparative purposes. Significant differences between groups were obtained for all studied parameters. The successful differentiation between some tissue groups suggests its potential use for carcinoma characterization. (E-mail: lorena.petrella@gmail. com) Ó 2012 World Federation for Ultrasound in Medicine & Biology. Key Words: Ultrasound biomicroscopy, Basal cell carcinoma, Statistical data analysis.
cable for the analysis of pigmented skin lesions. Stanley et al. (2007) developed an algorithm for delimiting contours in pigmented lesion images obtained by dermoscopy. Using laser confocal microscopy, spots of tissue are lightened with a focused beam and the backscatter light is used for generating the image planes. Applying this method, Goldgeirer et al. (2003) studied the evolution of basal cell carcinomas (BCCs) through a therapy process. In case of optical coherence tomography, the image is generated by infrared light backscattered by the tissue components. Jorgensen et al. (2008) studied BCC cases with this technique, visualizing the tumor nests as opaque structures. In phase-contrast X-ray microscopy, the association of an X-ray font with condenser lenses allows combining diffracted waves (from the studied object) and direct waves, being each other out of phase; unlike conventional X-rays, it provides border enhancement. A study using this method was developed by Son et al. (2008), where several structures from ex vivo BCC cases were differentiated. Pulsed terahertz technique employs electromagnetic waves in the range of 0.1–10 THz, which are reflected from tissues
INTRODUCTION In dermatology, the methods for the diagnosis of most cutaneous lesions consist of a clinical examination followed by biopsy and optic microscopy (OM) observation. This process enables a precise diagnosis because cellular and subcellular structures can be visualized by OM. Nevertheless, a number of noninvasive techniques have received growing attention in several medical fields. This is because modern medicine attempts to diminish both, the time consumption in diagnostic processes (simpler procedures) and the patients’ discomfort and risks (such as pain, infection and postinterventional care). Numerous principles used for these purposes are mentioned below and their main characteristics are presented in Table 1. A superficial image is obtained through dermoscopy by combining oil immersion, lighting and optical amplification (up to 103), which is more appliAddress correspondence to: Lorena Itatı Petrella, Laboratory of Ultrasound, National Institute of Metrology, Quality and Technology, Nossa Sra das Grac¸as Ave., n 50, Block 1, Duque de Caxias, Rio de Janeiro, Brazil, 25250-020. E-mail: [email protected] 1811
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Table 1. Principal image characteristics of noninvasive diagnostic techniques used in dermatology Noninvasive technique
Image plane orientation (related to skin surface)
Image depth
Spatial resolution
Dermoscopy Laser confocal microscopy Optical coherence tomography Phase contrast X-ray microscopy Pulsed terahertz UBM
Coplanar Coplanar Orthogonal Orthogonal Coplanar Orthogonal
Superficial Tens of mm About 1 mm Few mm Few mm Few mm
103 amplification Few mm Tens of mm Tens of nm Tens of mm Tens of mm
UBM 5 ultrasound biomicroscopy.
and used in image generation. In a study conducted by Wallace et al. (2004), an evaluation of the pulsed terahertz potential was made to differentiate between BCC cases and healthy skin. Using ultrasound biomicroscopy (UBM), the images are constructed from high-frequency acoustic signals backscattered from tissue (B-mode). In Petrella et al. (2010a), different BCC types were studied from UBM images and different structures composing the lesions were identified for most cases. The mentioned techniques are commonly applied to initially visualize the lesion area, aid in therapeutic planning or follow the evolution of a pathological process. As shown in Table 1, methods presenting better spatial resolutions commonly have minor image depth capabilities and depending on the lesion being studied, one of them will be most appropriate. On the other hand, the use of noninvasive techniques as primary diagnostic methods is still limited in dermatology, their resolutions being frequently insufficient for histologic characterization. Consequently, additional processing for obtaining quantitative information from noninvasive techniques (more than pure qualitative image observation), envisages the improvement of their diagnostic capabilities. For the particular case of UBM, the method is similar to conventional echographic systems but works at much higher frequencies (i.e., 20–60 MHz for most clinical applications) (Foster et al. 2000). The use of high frequencies improves the image spatial resolution, permitting scales of tens of micrometers to be obtained. In dermatologic applications, such resolutions are sufficient for exploring several healthy and anomalous cutaneous structures (Fornage et al. 1993; Vogt et al. 2010), although not for achieving cellular levels. The beam penetration is reduced in UBM because of the larger attenuation of acoustic waves at higher frequencies, limiting the depth of the visualization field to a few millimeters. Nevertheless, the penetration is usually sufficient for visualizing epidermal and dermal layers, as well as some portions of the hypodermis. Several benefits of UBM, such as the simplicity of the process involved on diagnostic, the short time required and the low aggressiveness, made the technique attractive for dermatologic applications. It explains the evolution of UBM applications in the last few years.
Quantitative analyses are being developed as additional means for tissue characterization and to reduce the subjectivities associated with purely qualitative image observations. These methods employ radio-frequency (RF) signals backscattered from tissues and acquired with A-mode scanning. The most frequently applied methods include acoustic, mechanical, spectral and statistical parameters’ computation. Acoustic parameters represent the wave properties influenced by medium propagation characteristics; parameters related to ultrasound speed, wave attenuation and backscatter are commonly studied. These parameters have been used in dermatology to analyze spatial structural variations in healthy skin (Lebertre et al. 2002; Raju and Srinivasan 2001) and to characterize anomalous conditions (Huang et al. 2007; Miyasaka et al. 2005). The mechanical properties of cutaneous tissues have been widely studied with ultrasound because of their relevant functional and aesthetic roles. These methods generally consist of submitting the skin to increasing controlled forces (like negative pressure or transverse stress) and measuring the variations in acoustic parameters along these stages (Gennisson et al. 2004; Pan et al. 1998; Vogt and Emert 2005). These mechanical parameters aid in the characterization of both physiologic processes (e.g., aging effects) and anomalous conditions. The study of statistical properties from acoustic backscattered signals has also been carried out in the analysis of cutaneous tissues. Several scatterers’ characteristics of the medium (e.g., sizes, cross-sections and distributions) together contribute to the statistical parameters’ values. Through statistical analysis, the media can be classified as Rayleigh, pre-Rayleigh or post-Rayleigh (Rician) (Shankar et al. 2001). Rayleigh media display a high concentration of randomly distributed scatterers with a uniform cross-section. Pre-Rayleigh media have either a low concentration of scatterers or randomly distributed cross-sections. Rician media display some periodicity in the distribution of the scatterers, related to wavelength multiples. Among the most frequently studied statistical parameters is the signal-to-noise ratio (SNR), which is computed directly from the amplitudes of the RF signals’ envelope.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
The amplitude values can also be used to construct histograms, which are then fitted to various probability density function (PDF) models. The shapes and amplitudes of the resultant PDF curves reflect various characteristics of the scatterers compounding the medium. The above statistical analysis has been used to characterize healthy and anomalous skin conditions. Vogt et al. (1998) constructed parametric images of SNR and texture parameters, from an in vivo melanoma at frequencies of 57 and 87 MHz. The SNR was able to differentiate lower scatterers’ concentration on the tumor region and differences were also observed for texture parameters. Comparing Rayleigh and K models, and working at a central frequency (fc) of 35 MHz, Lebertre et al. (2002) determined that K-PDF better represented healthy dermal tissue. Raju and Srinivasan (2002) studied the fitting of six PDF models (Rayleigh, Rician, K, Nakami, Weibull and generalized gamma [GG]) to dermal and hypodermal media in healthy skin. At an fc of 28 MHz, the best results were obtained using the Weibull and GG curves. One anomalous condition was studied by Raju et al. (2003), who compared healthy and contact dermatitis–affected skin using SNR and the K, Weibull, and GG PDF. With a 33 MHz system, the tissues for both skin conditions presented similar pre-Rayleigh characteristics. Pereyra and Batatia (2010) used a heavy-tailed Rayleigh PDF model to study cutaneous melanomas in vivo. They reported a good fit of this model to signal backscattered from skin tissues, as well as a fine differentiation between normal and tumoral tissues. BCCs, the most common malignant skin tumors, are derived from atypical proliferations of epidermal and adnexal keratinocytes, specifically from basaloid cells (Weedon et al. 2006). Their tumor nests are constituted by atypical cells organized in various patterns, such as lobules, columns, bands or cords, which give rise to the different BCC histologic types. The principal predisposing factor is recurring sunlight exposure and despite their low aggression, BCCs can metastasize if left untreated for a long period of time. This study was focused on BCCs because the large incidence of these lesions, that may be accentuated by the climatic characteristics found on tropical regions. The establishment of a fast, noninvasive diagnostic method could reduce patient discomfort and improve operation of health-care centers. An extensive literature search conducted by us did not find published reports of cutaneous carcinomas being examined using the statistical properties of ultrasonic backscattered signals. Since important structural differences exist between the collagen networks of healthy dermis and the atypical composition of carcinomatous skin, ultrasonic scatters’ properties are expected to diverge considerably between both tissue conditions. Therefore, an analysis of the
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statistical parameters should serve as a promising tool for their characterization. Accordingly, the main goal of the present work was to examine the ultrasonic scatter properties of BCCs by statistical parameters analysis, using a UBM system working at an fc of 45 MHz. The work has been conducted on ex vivo tissues because of the laboratorial characteristics and the procedure employed (as mentioned hereafter). Nevertheless, it is expected that the results here obtained test the method validity and can be understood as a first reference for future noninvasive (in vivo) applications. MATERIALS AND METHODS Tissue samples Ex vivo cutaneous tissue samples were obtained from volunteer patients who were suspected of cutaneous carcinoma. Patients were subjected to biopsy at the Dermatology Department of Gaffree and Guinle University Hospital (HUGG), Rio de Janeiro, Brazil. They were invited to participate after being informed of the purposes of the study. All experimental procedures were approved by the HUGG Ethical Committee and the National Committee for Ethics in Research. Biopsies were excised from different body regions, including the face, trunk and arms. A total of 27 BCC samples were analyzed and divided into two groups according to the tumor nest distribution (Fig. 1). In the BCC1 group (16 samples), the tumor nests were small, numerous, distributed along the dermis and surrounded by abundant stroma and inflammatory cell infiltrate (Fig.1a and b). In the BCC2 group (11 samples), atypical keratinocytes were compacted in a circumscribed region, whereas the stroma or inflammatory cell infiltrate was principally found around these regions (Fig. 1c and d). Seven non-tumoral (NT) skin samples were also obtained and used for comparative purposes. They corresponded to four healthy skin samples from biopsy specimens containing normal tissue around the lesions and three actinic keratosis samples excised under carcinoma suspicion (these are intraepidermal neoplasm, where not atypical keratinocytes or stroma were present on dermis). After excision, the tissue samples were preserved in formalin solution and then analyzed by UBM. They were subsequently studied by OM for diagnostic purposes in the Pathologic Anatomy Department of HUGG. UBM system The experimental UBM system used in the present study was constructed in the Biomedical Engineering Program of the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil (Fig. 2). The main characteristics of this system are shown in Table 2. The system can operate in A- or B-mode for RF signals acquisition and image
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Fig. 1. (a) and (b) Basal cell carcinoma (BCC) (corresponding to BCC1 group) with small tumor nests along the dermal thickness (arrows) and surrounded by stroma and inflammatory cell infiltrate. In the ultrasound biomicroscopy (UBM) image (a), the tumor nests are not delimited from the adjacent components, showing a heterogeneous echogenicity. (c) and (d) BCC (corresponding to BCC2 group) with the tumor nests compacted in a circumscribed region (arrows), and with little stroma or inflammatory cell infiltrate between them. In the UBM image (c), the tumor is delimited by a hypoechoic border. The lateral markers in the UBM images are 100 mm.
generation respectively. It contains a mono-element PVDF transducer (C190240; Capistrano Labs, San Clemente, CA, USA) that works in emitter-receptor mode and is excited by a monocycle pulse generator (AVB2-TB-C; Avtech Electrosystem, Ottawa, Ontario, Canada). The transducer is attached to an arm whose movement, with an arcscanning geometry during image acquisition, is imposed
by dc motor (314389; Maxon A-max, Sachseln, Switzerland). An optical encoder, coupled to the dc motor axis generates the synchronism pulses, which in turn act on both pulse generator and digitizer. The focal zone is positioned along the tissue depth (Z-direction) by a highprecision (10 mm) vertical platform (360-90; Newport, Irvine, CA, USA). The sample chamber is mounted over a high-precision (10 mm) horizontal platform (M-436A; Newport) that can move in the X and Y-directions. For B-mode image generation, backscattered signals captured by the transducer were conditioned by an RF amplifier (AU-1054; Miteq, Hauppauge, NY, USA) and then sent to a logarithmic amplifier (HLVA-100; FEMTO Messtechnik GmbH, Berlin, Germany) that provided the RF signal envelope. For RF signal acquisitions (A-mode scanning), the transducer face was kept parallel to the tissue surface and the RF amplifier output was not connected to the logarithmic amplifier (Fig. 2). The
Table 2. Principal UBM system characteristics
Fig. 2. Ultrasound biomicroscopy (UBM) system and tissue sample assemblage diagram. Gray arrows between the RF amplifier (RF Amp) and the digitizer represent the data path for image acquisitions. Black arrows represent the data path for RF signal acquisitions. Log Amp 5 logarithmic amplifier; RFAmp 5 RF amplifier; RF 5 radio-frequency; T 5 transducer.
Characteristic
Value
Unit
fc Axial resolution Lateral resolution Depth of field Focal distance
45 30 100 1.7 12
MHz mm mm mm mm
UBM 5 ultrasound biomicroscopy.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
logarithmic or RF amplifier outputs were connected to a digitizer board (NI PCI-5114; National Instrument, Austin, TX, USA) installed in a PC, which worked with a maximum sample frequency of 250 MHz and 8 bits of resolution. Two user interfaces were developed in the LabVIEW 7.0 (National Instruments Corporation, Austin, TX, USA) software for visualizing and saving the images and signals during acquisitions. Acquisition protocol During the acquisition process, a skin sample was positioned on a reflector disc with the epidermis at the top. This assemblage was immersed in an acrylic chamber filled with saline solution (with 0.9% of NaCL), which acted as a coupling medium between the skin and the transducer surface (Fig. 2). The solution and sample were kept at room temperature (24–30 C). The signal acquisition positions, which were located along a straight path, were first selected from the visualization of the B-mode images, acquiring sequences of parallel planes in two orthogonal directions. For image visualization the tissue surface was first maintained free, and then covered by a polymer film to keep the sample in place during signal acquisition (being previously proved that the polymer film did not represent significant influence on the backscattered signal). During A-mode acquisitions, the transducer surface was maintained parallel to the horizontal plane and the focus was located inside the dermis depth. The acquired signal window was selected to contain only the backscatter signal from the tissue, avoiding echoes reflected in the superior and inferior interfaces. For successive acquisitions, the sample was dislocated by moving the horizontal platform along the X-axis with a spacing of 50 mm. The total distance covered varied with the sample dimensions, from 2.0–3.4 mm (equivalent to 40–64 signals per sample). Each saved signal was the time average of 100 RF backscattered signals from each position, to reduce the noise level. After the signals were acquired, they were processed for compensating from attenuation and diffraction effects; this makes the backscattered signals’ information independent of the scatterers’ positions. For attenuation compensation, a method presented in (Ye et al. 1995) was implemented. The signals were segmented and a Fourier transform was then computed. Each segment was multiplied by the term exp(ai(f)), where ai(f) is the attenuation coefficient (calculated in previous stages) that depends on the distance travelled by the acoustic wave up to the central point of each segment (i). The inverse Fourier transform was applied and time signals were reconstructed from each compensated segment. For diffraction correction, a diffraction curve was computed based on the geometric characteristics of the ultrasound beam, as proposed by Machado and Foster
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(2001). Subsequently, the ratio between the signal (compensated from attenuation effects only) and the curve was computed, obtaining the fully compensated RF signal. Their envelopes were calculated using the Hilbert transform. Afterwards, a two-dimensional (2-D) frame was reconstructed wherein the horizontal and vertical axes corresponded to the acquisition path and the tissue depth respectively. From the resultant image frame, a rectangular region-of-interest (ROI) was selected, containing the neoplasm region for BCC samples and the dermal collagen network for NT samples (excluding evident cutaneous annexes or epidermal parts). The image ROI was sampled to reduce the correlation between adjacent pixels, giving a minimum spacing of half of the lateral resolution (approximately 100/2 mm) between pixels in the horizontal direction and of half of the axial resolution (approximately 30/2 mm) between those in the vertical direction. This sampled ROI ($800 pixels in size) was finally used to compute the statistical parameters. The user interfaces employed for all the mentioned procedures were developed with LabVIEW 7.0 (National Instruments Corporation) software. Computation of statistical parameters The SNR, and the Weibull and GG PDF parameters were computed and analyzed. The SNR was directly computed from the amplitudes of the signals’ envelope. The PDF parameters were obtained from the corresponding curves adjusted to a histogram constructed from the amplitudes. The PDF models’ selection was made based on the results reported by other authors (Raju and Srinivasan 2002). The SNR was obtained from eqn (1): hRi SNR 5 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 hR2 i2hRi
(1)
where R represents the amplitude of the signals’ envelope and the symbol h,i represents the mean value. When the medium meets the Rayleigh conditions, the SNR is close to 1.91. When the SNR is either lower or higher than 1.91, the medium displays pre- or post-Rayleigh characteristics respectively (Raju et al. 2003). The Weibull PDF is defined by eqn (2): bW bW 21 2 R bW R pðRÞ 5 , ,e aW for aw and bw .0: aW aW (2) Here aW and bW represent the PDF amplitude and shape respectively. These parameters were computed by the maximum likelihood method (ML), from the loglikelihood of the function given in eqn (2) (Raju et al. 2003):
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LðR1 ; :::; RN ; aW ; bW Þ 5 N,lnðbW Þ1ðbW 21Þ,
N X i51
where N is the number of pixels compounding the ROI. The expression given in eqn (3) was then derived in terms of aW and equated to zero, obtaining: !1=bw PN bw i 5 1 Ri aW 5 : (4) N The aW value for each arbitrary bW value was obtained from eqn (4). The pair (aW, bW) that maximizes eqn (3), which is the one that best fits the PDF to the histogram shape, was selected to represent the medium. In the Weibull model, when bW is approximately 2, the medium presents Rayleigh characteristics. When bW is 0–2, the medium presents pre-Rayleigh characteristics. When bW is .2, the medium presents Rician characteristics. The PDF of the GG model is defined as (Raju and Srinivasan 2002): yGG R cGG ,RðcGG ,yGG 21Þ 2 aGG pðRÞ 5 ðcGG ,yGG Þ ,e (5) aGG ,GðyGG Þ for aGG ; yGG and cGG .0; where G is the gamma function, aGG represents the amplitude parameter and yGG and cGG represent the shape parameters for the right and left PDF tails, almost independently. In this model, the parameters were also computed using the ML method (Raju et al. 2003), the log-likelihood function being defined by:
Ri 2
b N X Ri W 2N,bW ,lnðaW Þ; aW i51
PN i51
aGG 5
N,yGG
yGG 5 2 cGG > > :
PN
i 5 1 lnðRi Þ 2 N
921
!> PN > cGG = i 5 1 Ri ,lnðRi Þ PN cGG > > ; i 5 1 Ri
(7)
(8)
RESULTS The parameters chosen to be analyzed were those related to PDF curve shape (i.e., SNR, bW, cGG and yGG), ignoring the ones related to PDF curve amplitude. This selection was made because some system conditions could cause variations in signal levels for acquisitions made on different days, thus, compromising the comparison of these parameters between samples. The values obtained for all samples of each tissue group (BCC1, BCC2 and NT) and their mean values are shown in Figures 4–7. All tissue groups displayed preRayleigh characteristics, with SNR values of ,1.91 (Fig. 4). The lowest mean value was obtained for the
N P i51
lnðRi Þ2
2N,cGG ,yGG ,lnðaGG Þ2N,lnðGðyGG ÞÞ
8 > > <
!1=cGG
Finally, the set (aGG, yGG, cGG) that maximized eqn (6) (i.e., the best adjustment to the histogram profile) was selected to represent the medium characteristics. In the GG model, when yGG 5 1 and cGG 5 2, the medium presents Rayleigh characteristics. In the particular case where yGG 5 1, the GG PDF is equivalent to the Weibull PDF. The computational program for statistical parameter calculus was developed using the LabVIEW 7.0 (National Instruments Corporation) software. Examples of histogram adjustment with the Weibull and GG curves are shown in Figure 3.
LðR1 ; .; RN ; aGG ; yGG ; cGG Þ 5 N,lnðcGG Þ1ðcGG ,yGG 21Þ,
In this case, the cGG parameter was varied arbitrarily and the corresponding yGG and cGG values were obtained from eqns (7) and (8) respectively:
Rci GG
(3)
N P
i51
Ri aGG
cGG (6)
BCC1 group, whereas the BCC2 and NT groups displayed higher and more similar value ranges. As shown in Figures 5 and 6, the bW and cGG parameters followed the tendencies observed for SNR. The yGG followed an opposite tendency (Fig. 7), being the highest mean value obtained for the BCC1 group. When differences between the groups were analyzed using the Kruskal Wallis test (applicable to a small number of samples) with a 95% level of confidence, significant differences were found for all the studied parameters.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
Fig. 3. Histogram obtained from one selected region of interest (ROI) and the adjusted Weibull PDF (continuous line) and GG PDF (dotted line) obtained by the maximum likelihood method. PDF 5 probability density function; GG 5 generalized gamma.
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Fig. 5. The bW values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
In the present work, the characteristics (concentrations, cross-sections and distributions) of acoustic scatterers present on non-tumoral skin and cutaneous carcinomas (specifically BCCs) were analyzed using a statistical approach. All tissue samples showed pre-Rayleigh characteristics (SNR , 1.91). Significant differences between tissue groups were observed for all the studied parameters. The SNR mean value observed for the BCC1 group was noticeably lower than that obtained for the NT group; no important differences were observed between the NT and BCC2 groups. The lower SNR values and the more obvious pre-Rayleigh characteristics of the BCC1 group can be explained as follows. The scatterers’ reduced concentration was produced by the abundant amorphous stroma surrounding the tumor nests, as well as by the presence of inflammatory cells in the affected region, which are smaller than keratinocytes. Nonhomogeneous scatterer cross-sections were produced because the tumor nests
could not be well distinguished from the surrounding components when the ROI was defined, thus, leading to different scatterer types within it. Conversely, the tumor nests were more easily delimited in the BCC2 group. In this group, the nests primarily contained one scatterer type (atypical keratinocytes), with a few amorphous stroma inside the ROI. In the NT group, the ROI scatterers primarily consisted in a dense collagen fiber network. Similar observations were seen for bW and cGG, while yGG followed an opposite tendency because of the same histologic characteristics. Analogous works conducted with healthy skin have also shown pre-Rayleigh characteristics, although the parameters tended to be more pre-Rayleigh–like than our results (Table 3). These differences are perhaps owing to variations in the methodology employed (e.g., lower frequency levels, differences between in vivo and ex vivo tissue conditions, the body region from where the signal acquisitions were made, etc.). To our knowledge, based on an extensive literature review, no study has been found regarding statistical parameter analysis for cutaneous carcinomas; therefore, those results could not be compared with literature values.
Fig. 4. SNR values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
Fig. 6. The cGG values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
DISCUSSION AND CONCLUSION
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Fig. 7. The yGG values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
A typical diagnostic procedure starts with a clinical examination, after which some lesion possibilities are discarded and other considered, as commonly occurs for BCC cases. If the procedure is followed by a UBM imaging, additional differentiation will be possible on diagnostic process, since some BCC characteristics are identifiable by this technique (Petrella et al. 2010a). Further studies can still be necessary after direct and ultrasonic visualization; thus, a quantitative statistical approach was proposed here as a complementary tool. The statistical differences obtained for BCC1 group are important, since the presence of tumor nests are less evident on UBM images (compared with BCC2 group), being primordial additional quantitative procedures. We encompass the analysis to the comparison of BCC cases with healthy skin and actinic keratosis, to first evaluate the method usefulness. Once the results show relevance, other lesions (whose clinical aspects and UBM image characteristics can be similar to that found on BCCs) must necessarily be considered, to give stronger diagnostic significance to the findings. In this sense, because a larger number of patients must be considered, in vivo studies will be more feasible in future. Ex vivo tissue samples were used in this work because of the reasons that follow. First, the UBM system was not designed for human in vivo studies. Second, a large volume of image and signals was acquired, resulting in a long procedure (of few hours), which was impracticable for studies with patients. Finally, ex vivo
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measurements allow a better control over some experimental variables (like tissue movements). For applying the method on further noninvasive analysis of cutaneous carcinomas, the results here observed must be compared with in vivo studies. In a first approach it is reasonable to suppose that the differences of parameters values, obtained for ex vivo carcinomatous and not tumoral skin, should also appear for in vivo observations. This is because the scatterers composition, sizes and distribution, which define the statistical properties of ultrasonic signals, are reasonably maintained when the excised tissue is preserved appropriately. Moreover, other kinds of quantitative parameters can be analyzed together with the statistical ones to improve tissue characterization. One approach is the study of acoustic parameters like attenuation, speed or backscattering of acoustic waves that has already been conducted by us. We previously computed acoustic parameters following the method proposed by Ye et al. (1995), which employs signals backscattered along all tissue depth (in our case epidermis, dermis and hypodermis), including parts affected by tumor or not. We did not find significant differences between tissue groups, probably because the acoustic properties from the tumor itself had been obscured by adjacent tissue influence. Nevertheless, other methods to calculate acoustic parameters can be considered in the future. On the other side, Petrella et al. (2010b) made a study of the integrated backscatter coefficient, which represents the backscattered power normalized to the incident intensity as a function of tissue depth. This study was restricted to BCC types whose characteristics are more similar between patients (superficial BCC and Bowen disease), obtaining optimistic results. In the future, the analysis should be extended to more complex BCC types. In addition, the analysis of texture characteristics obtained from ultrasound image processing methods are presently being developed based on works applied to other medical areas (Zhan and Shen 2006). In the near future, we expect to apply spectral analysis to provide information about the scatterers’ shapes (Lizzi et al. 1996). In conclusion, the statistical parameters were able to differentiate one carcinomatous skin condition from
Table 3. Comparison of parameter values obtained in different works for healthy skin Author
fc [MHz]
SNR*
bW*
cGG*
yGG*
Body region
Study
Raju and Srinivasan 2002 Raju et al. 2003 This work**
28 33 45
1.4 1.36 1.72
1.48 1.42 1.79
0.85 0.94 1.44
3.03 2.34 1.55
Fingertip Forearms Arm and forearm
In vivo In vivo Ex vivo
bW 5 Weibull; cGG 5 generalized gamma; yGG 5 generalized gamma; SNR 5 signal-to-noise ratio. * Mean values. ** Considering four healthy skin samples studied.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
non-tumoral tissue, presenting reduced scatterers’ concentration with nonhomogeneous cross-sections. We predict that this capability could be used for diagnostic purposes in many cutaneous lesions presenting different scatterers’ patterns. Acknowledgments—The authors are grateful for the financial support provided by CAPES/PROEX, CNPq and FAPERJ, and for the collaboration of staff in the Dermatology and Pathologic Anatomy Departments of HUGG.
REFERENCES Fornage BD, Mc Gavran MH, Duciv M, Waldron CA. Imaging of the skin with 20 MHz US. Radiology 1993;189:69–76. Foster FS, Pavlin CJ, Harasiewicz KA, Christopher DA, Turnbull DH. Advances in ultrasound biomicroscopy. Ultrasound Med Biol 2000;26:1–27. Goldgeirer M, Fox CA, Zavislan JM, Harris D, Gonzalez S. Noninvasive imaging, treatment, and microscopic confirmation of clearance of basal cell carcinoma. Dermatol Surg 2003;29:205–210. Gennisson J-L, Baldeweck T, Tanter M, Catheline S, Fink M, Sandrin L, Cornillon C, Querleux B. Assessment of elastic parameters of human skin using dynamic elastography. IEEE Trans Ultrason Ferroelectr Freq Cont 2004;51:980–989. Huang YP, Zheng YP, Leung SF, Choi APC. High frequency ultrasound assessment of skin fibrosis: Clinical results. Ultrasound Med Biol 2007;33:1191–1198. Jorgensen TM, Tycho A, Mogensen M, Bjerring P, Jemec GBE. Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography. Skin Res Technol 2008;14:364–369. Lebertre M, Ossant F, Vaillant L, Diridollou S, Patat F. Spatial variation of acoustic parameters in human skin: An in vitro study between 22 and 45 MHz. Ultrasound Med Biol 2002;28:599–615. Lizzi FL, Astor M, Kalisz A, Liu T, Coleman DJ, Silverman R, Ursea R, Rondeau M. Ultrasonic spectral analysis for assays of different scatterer morphologies: Theory and very-high frequency clinical results. IEEE Ultrasonic Symp 1996;1:1155–1159. Machado JC, Foster FS. Ultrasonic backscatter integrated coefficient profiling of human coronary arteries in vitro. IEEE Trans Ultrason Ferroelectr Freq Control 2001;48:17–27. Miyasaka M, Sakai S, Kusaka A, Endo Y, Kobayashi M, Kobayashi K, Hozumi N, Tanino R. Ultrasonic tissue characterization of photo damaged skin by scanning acoustic microscopy. Tokai J Exp Clin Med 2005;30:217–225. Pan L, Zan L, Foster FS. Ultrasonic and viscoelastic properties of the skin under transverse mechanical stress in vitro. Ultrasound Med Biol 1998;24:995–1007.
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Pereyra MA, Batatia H. An alpha-stable model for ultrasound speckle statistics in skin. 32nd Annual International Conference of the IEEE EMBS 2010;1:4715–4718. Petrella LI, Martins CJ, Valle HA, Issa PR, Pereira WCA, Machado JC. Study of cutaneous cell carcinomas ex vivo by ultrasonic biomicroscopic images. Skin Res Tecnol 2010a;16:422–427. Petrella LI, Pereira WCA, Valle HA, Issa PR, Martins CJ, Machado JC. Study of superficial basal cell carcinoma and Bowen disease by qualitative and quantitative ultrasonic biomicroscopy approach. 32nd Annual International Conference of the IEEE EMBS 2010b;1: 5999–6002. Raju BI, Srinivasan MA. High-frequency ultrasonic attenuation and backscatter coefficients of in vivo normal human dermis and subcutaneous fat. Ultrasound Med Biol 2001;27:1543–1556. Raju BI, Srinivasan MA. Statistics of envelope of high-frequency ultrasonic backscatter from human skin in vivo. IEEE Trans Ultrason Ferroelectr Freq Control 2002;49:871–882. Raju BI, Swindells KJ, Gonzalez S, Srinivasan MA. Quantitative ultrasonic methods for characterizations of skin lesions in vivo. Ultrasound Med Biol 2003;29:825–838. Shankar PM, Dumane VA, Reid JM, Genis V, Forsberg F, Piccoli CW, Goldberg BB. Classification of ultrasonic B-mode images of breast masses using Nakagami distribution. IEEE Trans Ultrason Ferroelectr Freq Control 2001;48:569–580. Son SW, Park SY, Park GM, Ha SH, Lee GW, Lee OS, Hwu Y, Kim AR, Je JH, Oh CH. Ex vivo imaging of basal cell carcinoma using synchrotron phase-contrast X-ray microscopy. Skin Res Technol 2008;14:13–17. Stanley RJ, Stoecker WV, Moss RH. A relative colour approach to colour discrimination for malignant melanoma detection in dermoscopy images. Skin Res Technol 2007;13:62–72. Vogt M, Emert H, el Gammal S, Kaspar K, Hoffmann K, Altmeyer P. Structural analysis of the skin using high-frequency, broadband ultrasound in the range from 30 to 140 MHz. IEEE Ultrason Symp Proc 1998;1:1685–1688. Vogt M, Emert H. Development and evaluation of a high-frequency ultrasound-based system for in vivo strain imaging of the skin. IEEE Trans Ultrason Ferroelectr Freq Control 2005;52:375–385. Vogt M, Opretzka J, Perrey C, Emert H. Ultrasonic microscanning. Proc Inst Mech Eng Part H J Eng Med 2010;224:225–240. Wallace VP, Fitzgerald AJ, Shankar S, Agan NF, Pye R, Cluff J, Arnone DD. Terahertz pulsed imaging of basal cell carcinoma ex vivo and in vivo. Br J Dermatol 2004;151:424–432. Weedon D, Marks R, Kao GF, Harwood CA. Keratinocytic tumors. In: Leboit PE, Burg G, Weedon G, Sarasain A, eds. Lyon: IARC Press, 2006:9–33. Ye SG, Harasiewicz KA, Pavlin CJ, Foster FS. Ultrasound characterization of normal ocular tissue in the frequency range from 50 to 100 MHz. IEEE Trans Ultrason Ferroelectr Freq Control 1995;42:8–14. Zhan Y, Shen D. Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method. IEEE Trans Med Imaging 2006;25:256–272.
http://dx.doi.org/10.1016/j.ultrasmedbio.2012.06.001
d
Original Contribution STATISTICAL ANALYSIS OF HIGH FREQUENCY ULTRASONIC BACKSCATTERED SIGNALS FROM BASAL CELL CARCINOMAS LORENA ITATI PETRELLA,*y HELIOMAR DE AZEVEDO VALLE,z PAULO ROBERTO ISSA,x ~O CARLOS MACHADO,yjj and WAGNER COELHO A. PEREIRAy CARLOS JOSE MARTINS,x JOA
* Laboratory of Ultrasound, National Institute of Metrology, Quality and Technology, Duque de Caxias, RJ, Brazil; y Biomedical Engineering Program, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil; z Pathologic Anatomy Department, Gaffree and Guinle University Hospital, Rio de Janeiro, RJ, Brazil; x Dermatology Department, Gaffree and Guinle University Hospital, Rio de Janeiro, RJ, Brazil; and jj Post-Graduation Program in Surgical Sciences, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil (Received 25 October 2011; revised 21 April 2012; in final form 4 June 2012)
Abstract—A statistical approach was implemented in the study of histologic characteristics from ex vivo basal cell carcinomas, based on the properties of backscattered acoustic waves, for the purpose of evaluating the method as a diagnostic tool. The study was developed using an ultrasound biomicroscope working at a frequency of 45 MHz. The parameters examined were signal-to-noise ratio (SNR), and shape parameters from the Weibull (bW) and generalized gamma (cGG and yGG) probability density functions. Twenty-seven carcinomatous skin samples were obtained from volunteer patients and classified into two groups (BCC1 and BCC2) based on the distribution patterns of their tumor nests; also, seven non-tumoral samples were used for comparative purposes. Significant differences between groups were obtained for all studied parameters. The successful differentiation between some tissue groups suggests its potential use for carcinoma characterization. (E-mail: lorena.petrella@gmail. com) Ó 2012 World Federation for Ultrasound in Medicine & Biology. Key Words: Ultrasound biomicroscopy, Basal cell carcinoma, Statistical data analysis.
cable for the analysis of pigmented skin lesions. Stanley et al. (2007) developed an algorithm for delimiting contours in pigmented lesion images obtained by dermoscopy. Using laser confocal microscopy, spots of tissue are lightened with a focused beam and the backscatter light is used for generating the image planes. Applying this method, Goldgeirer et al. (2003) studied the evolution of basal cell carcinomas (BCCs) through a therapy process. In case of optical coherence tomography, the image is generated by infrared light backscattered by the tissue components. Jorgensen et al. (2008) studied BCC cases with this technique, visualizing the tumor nests as opaque structures. In phase-contrast X-ray microscopy, the association of an X-ray font with condenser lenses allows combining diffracted waves (from the studied object) and direct waves, being each other out of phase; unlike conventional X-rays, it provides border enhancement. A study using this method was developed by Son et al. (2008), where several structures from ex vivo BCC cases were differentiated. Pulsed terahertz technique employs electromagnetic waves in the range of 0.1–10 THz, which are reflected from tissues
INTRODUCTION In dermatology, the methods for the diagnosis of most cutaneous lesions consist of a clinical examination followed by biopsy and optic microscopy (OM) observation. This process enables a precise diagnosis because cellular and subcellular structures can be visualized by OM. Nevertheless, a number of noninvasive techniques have received growing attention in several medical fields. This is because modern medicine attempts to diminish both, the time consumption in diagnostic processes (simpler procedures) and the patients’ discomfort and risks (such as pain, infection and postinterventional care). Numerous principles used for these purposes are mentioned below and their main characteristics are presented in Table 1. A superficial image is obtained through dermoscopy by combining oil immersion, lighting and optical amplification (up to 103), which is more appliAddress correspondence to: Lorena Itatı Petrella, Laboratory of Ultrasound, National Institute of Metrology, Quality and Technology, Nossa Sra das Grac¸as Ave., n 50, Block 1, Duque de Caxias, Rio de Janeiro, Brazil, 25250-020. E-mail: [email protected] 1811
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Table 1. Principal image characteristics of noninvasive diagnostic techniques used in dermatology Noninvasive technique
Image plane orientation (related to skin surface)
Image depth
Spatial resolution
Dermoscopy Laser confocal microscopy Optical coherence tomography Phase contrast X-ray microscopy Pulsed terahertz UBM
Coplanar Coplanar Orthogonal Orthogonal Coplanar Orthogonal
Superficial Tens of mm About 1 mm Few mm Few mm Few mm
103 amplification Few mm Tens of mm Tens of nm Tens of mm Tens of mm
UBM 5 ultrasound biomicroscopy.
and used in image generation. In a study conducted by Wallace et al. (2004), an evaluation of the pulsed terahertz potential was made to differentiate between BCC cases and healthy skin. Using ultrasound biomicroscopy (UBM), the images are constructed from high-frequency acoustic signals backscattered from tissue (B-mode). In Petrella et al. (2010a), different BCC types were studied from UBM images and different structures composing the lesions were identified for most cases. The mentioned techniques are commonly applied to initially visualize the lesion area, aid in therapeutic planning or follow the evolution of a pathological process. As shown in Table 1, methods presenting better spatial resolutions commonly have minor image depth capabilities and depending on the lesion being studied, one of them will be most appropriate. On the other hand, the use of noninvasive techniques as primary diagnostic methods is still limited in dermatology, their resolutions being frequently insufficient for histologic characterization. Consequently, additional processing for obtaining quantitative information from noninvasive techniques (more than pure qualitative image observation), envisages the improvement of their diagnostic capabilities. For the particular case of UBM, the method is similar to conventional echographic systems but works at much higher frequencies (i.e., 20–60 MHz for most clinical applications) (Foster et al. 2000). The use of high frequencies improves the image spatial resolution, permitting scales of tens of micrometers to be obtained. In dermatologic applications, such resolutions are sufficient for exploring several healthy and anomalous cutaneous structures (Fornage et al. 1993; Vogt et al. 2010), although not for achieving cellular levels. The beam penetration is reduced in UBM because of the larger attenuation of acoustic waves at higher frequencies, limiting the depth of the visualization field to a few millimeters. Nevertheless, the penetration is usually sufficient for visualizing epidermal and dermal layers, as well as some portions of the hypodermis. Several benefits of UBM, such as the simplicity of the process involved on diagnostic, the short time required and the low aggressiveness, made the technique attractive for dermatologic applications. It explains the evolution of UBM applications in the last few years.
Quantitative analyses are being developed as additional means for tissue characterization and to reduce the subjectivities associated with purely qualitative image observations. These methods employ radio-frequency (RF) signals backscattered from tissues and acquired with A-mode scanning. The most frequently applied methods include acoustic, mechanical, spectral and statistical parameters’ computation. Acoustic parameters represent the wave properties influenced by medium propagation characteristics; parameters related to ultrasound speed, wave attenuation and backscatter are commonly studied. These parameters have been used in dermatology to analyze spatial structural variations in healthy skin (Lebertre et al. 2002; Raju and Srinivasan 2001) and to characterize anomalous conditions (Huang et al. 2007; Miyasaka et al. 2005). The mechanical properties of cutaneous tissues have been widely studied with ultrasound because of their relevant functional and aesthetic roles. These methods generally consist of submitting the skin to increasing controlled forces (like negative pressure or transverse stress) and measuring the variations in acoustic parameters along these stages (Gennisson et al. 2004; Pan et al. 1998; Vogt and Emert 2005). These mechanical parameters aid in the characterization of both physiologic processes (e.g., aging effects) and anomalous conditions. The study of statistical properties from acoustic backscattered signals has also been carried out in the analysis of cutaneous tissues. Several scatterers’ characteristics of the medium (e.g., sizes, cross-sections and distributions) together contribute to the statistical parameters’ values. Through statistical analysis, the media can be classified as Rayleigh, pre-Rayleigh or post-Rayleigh (Rician) (Shankar et al. 2001). Rayleigh media display a high concentration of randomly distributed scatterers with a uniform cross-section. Pre-Rayleigh media have either a low concentration of scatterers or randomly distributed cross-sections. Rician media display some periodicity in the distribution of the scatterers, related to wavelength multiples. Among the most frequently studied statistical parameters is the signal-to-noise ratio (SNR), which is computed directly from the amplitudes of the RF signals’ envelope.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
The amplitude values can also be used to construct histograms, which are then fitted to various probability density function (PDF) models. The shapes and amplitudes of the resultant PDF curves reflect various characteristics of the scatterers compounding the medium. The above statistical analysis has been used to characterize healthy and anomalous skin conditions. Vogt et al. (1998) constructed parametric images of SNR and texture parameters, from an in vivo melanoma at frequencies of 57 and 87 MHz. The SNR was able to differentiate lower scatterers’ concentration on the tumor region and differences were also observed for texture parameters. Comparing Rayleigh and K models, and working at a central frequency (fc) of 35 MHz, Lebertre et al. (2002) determined that K-PDF better represented healthy dermal tissue. Raju and Srinivasan (2002) studied the fitting of six PDF models (Rayleigh, Rician, K, Nakami, Weibull and generalized gamma [GG]) to dermal and hypodermal media in healthy skin. At an fc of 28 MHz, the best results were obtained using the Weibull and GG curves. One anomalous condition was studied by Raju et al. (2003), who compared healthy and contact dermatitis–affected skin using SNR and the K, Weibull, and GG PDF. With a 33 MHz system, the tissues for both skin conditions presented similar pre-Rayleigh characteristics. Pereyra and Batatia (2010) used a heavy-tailed Rayleigh PDF model to study cutaneous melanomas in vivo. They reported a good fit of this model to signal backscattered from skin tissues, as well as a fine differentiation between normal and tumoral tissues. BCCs, the most common malignant skin tumors, are derived from atypical proliferations of epidermal and adnexal keratinocytes, specifically from basaloid cells (Weedon et al. 2006). Their tumor nests are constituted by atypical cells organized in various patterns, such as lobules, columns, bands or cords, which give rise to the different BCC histologic types. The principal predisposing factor is recurring sunlight exposure and despite their low aggression, BCCs can metastasize if left untreated for a long period of time. This study was focused on BCCs because the large incidence of these lesions, that may be accentuated by the climatic characteristics found on tropical regions. The establishment of a fast, noninvasive diagnostic method could reduce patient discomfort and improve operation of health-care centers. An extensive literature search conducted by us did not find published reports of cutaneous carcinomas being examined using the statistical properties of ultrasonic backscattered signals. Since important structural differences exist between the collagen networks of healthy dermis and the atypical composition of carcinomatous skin, ultrasonic scatters’ properties are expected to diverge considerably between both tissue conditions. Therefore, an analysis of the
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statistical parameters should serve as a promising tool for their characterization. Accordingly, the main goal of the present work was to examine the ultrasonic scatter properties of BCCs by statistical parameters analysis, using a UBM system working at an fc of 45 MHz. The work has been conducted on ex vivo tissues because of the laboratorial characteristics and the procedure employed (as mentioned hereafter). Nevertheless, it is expected that the results here obtained test the method validity and can be understood as a first reference for future noninvasive (in vivo) applications. MATERIALS AND METHODS Tissue samples Ex vivo cutaneous tissue samples were obtained from volunteer patients who were suspected of cutaneous carcinoma. Patients were subjected to biopsy at the Dermatology Department of Gaffree and Guinle University Hospital (HUGG), Rio de Janeiro, Brazil. They were invited to participate after being informed of the purposes of the study. All experimental procedures were approved by the HUGG Ethical Committee and the National Committee for Ethics in Research. Biopsies were excised from different body regions, including the face, trunk and arms. A total of 27 BCC samples were analyzed and divided into two groups according to the tumor nest distribution (Fig. 1). In the BCC1 group (16 samples), the tumor nests were small, numerous, distributed along the dermis and surrounded by abundant stroma and inflammatory cell infiltrate (Fig.1a and b). In the BCC2 group (11 samples), atypical keratinocytes were compacted in a circumscribed region, whereas the stroma or inflammatory cell infiltrate was principally found around these regions (Fig. 1c and d). Seven non-tumoral (NT) skin samples were also obtained and used for comparative purposes. They corresponded to four healthy skin samples from biopsy specimens containing normal tissue around the lesions and three actinic keratosis samples excised under carcinoma suspicion (these are intraepidermal neoplasm, where not atypical keratinocytes or stroma were present on dermis). After excision, the tissue samples were preserved in formalin solution and then analyzed by UBM. They were subsequently studied by OM for diagnostic purposes in the Pathologic Anatomy Department of HUGG. UBM system The experimental UBM system used in the present study was constructed in the Biomedical Engineering Program of the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil (Fig. 2). The main characteristics of this system are shown in Table 2. The system can operate in A- or B-mode for RF signals acquisition and image
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Fig. 1. (a) and (b) Basal cell carcinoma (BCC) (corresponding to BCC1 group) with small tumor nests along the dermal thickness (arrows) and surrounded by stroma and inflammatory cell infiltrate. In the ultrasound biomicroscopy (UBM) image (a), the tumor nests are not delimited from the adjacent components, showing a heterogeneous echogenicity. (c) and (d) BCC (corresponding to BCC2 group) with the tumor nests compacted in a circumscribed region (arrows), and with little stroma or inflammatory cell infiltrate between them. In the UBM image (c), the tumor is delimited by a hypoechoic border. The lateral markers in the UBM images are 100 mm.
generation respectively. It contains a mono-element PVDF transducer (C190240; Capistrano Labs, San Clemente, CA, USA) that works in emitter-receptor mode and is excited by a monocycle pulse generator (AVB2-TB-C; Avtech Electrosystem, Ottawa, Ontario, Canada). The transducer is attached to an arm whose movement, with an arcscanning geometry during image acquisition, is imposed
by dc motor (314389; Maxon A-max, Sachseln, Switzerland). An optical encoder, coupled to the dc motor axis generates the synchronism pulses, which in turn act on both pulse generator and digitizer. The focal zone is positioned along the tissue depth (Z-direction) by a highprecision (10 mm) vertical platform (360-90; Newport, Irvine, CA, USA). The sample chamber is mounted over a high-precision (10 mm) horizontal platform (M-436A; Newport) that can move in the X and Y-directions. For B-mode image generation, backscattered signals captured by the transducer were conditioned by an RF amplifier (AU-1054; Miteq, Hauppauge, NY, USA) and then sent to a logarithmic amplifier (HLVA-100; FEMTO Messtechnik GmbH, Berlin, Germany) that provided the RF signal envelope. For RF signal acquisitions (A-mode scanning), the transducer face was kept parallel to the tissue surface and the RF amplifier output was not connected to the logarithmic amplifier (Fig. 2). The
Table 2. Principal UBM system characteristics
Fig. 2. Ultrasound biomicroscopy (UBM) system and tissue sample assemblage diagram. Gray arrows between the RF amplifier (RF Amp) and the digitizer represent the data path for image acquisitions. Black arrows represent the data path for RF signal acquisitions. Log Amp 5 logarithmic amplifier; RFAmp 5 RF amplifier; RF 5 radio-frequency; T 5 transducer.
Characteristic
Value
Unit
fc Axial resolution Lateral resolution Depth of field Focal distance
45 30 100 1.7 12
MHz mm mm mm mm
UBM 5 ultrasound biomicroscopy.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
logarithmic or RF amplifier outputs were connected to a digitizer board (NI PCI-5114; National Instrument, Austin, TX, USA) installed in a PC, which worked with a maximum sample frequency of 250 MHz and 8 bits of resolution. Two user interfaces were developed in the LabVIEW 7.0 (National Instruments Corporation, Austin, TX, USA) software for visualizing and saving the images and signals during acquisitions. Acquisition protocol During the acquisition process, a skin sample was positioned on a reflector disc with the epidermis at the top. This assemblage was immersed in an acrylic chamber filled with saline solution (with 0.9% of NaCL), which acted as a coupling medium between the skin and the transducer surface (Fig. 2). The solution and sample were kept at room temperature (24–30 C). The signal acquisition positions, which were located along a straight path, were first selected from the visualization of the B-mode images, acquiring sequences of parallel planes in two orthogonal directions. For image visualization the tissue surface was first maintained free, and then covered by a polymer film to keep the sample in place during signal acquisition (being previously proved that the polymer film did not represent significant influence on the backscattered signal). During A-mode acquisitions, the transducer surface was maintained parallel to the horizontal plane and the focus was located inside the dermis depth. The acquired signal window was selected to contain only the backscatter signal from the tissue, avoiding echoes reflected in the superior and inferior interfaces. For successive acquisitions, the sample was dislocated by moving the horizontal platform along the X-axis with a spacing of 50 mm. The total distance covered varied with the sample dimensions, from 2.0–3.4 mm (equivalent to 40–64 signals per sample). Each saved signal was the time average of 100 RF backscattered signals from each position, to reduce the noise level. After the signals were acquired, they were processed for compensating from attenuation and diffraction effects; this makes the backscattered signals’ information independent of the scatterers’ positions. For attenuation compensation, a method presented in (Ye et al. 1995) was implemented. The signals were segmented and a Fourier transform was then computed. Each segment was multiplied by the term exp(ai(f)), where ai(f) is the attenuation coefficient (calculated in previous stages) that depends on the distance travelled by the acoustic wave up to the central point of each segment (i). The inverse Fourier transform was applied and time signals were reconstructed from each compensated segment. For diffraction correction, a diffraction curve was computed based on the geometric characteristics of the ultrasound beam, as proposed by Machado and Foster
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(2001). Subsequently, the ratio between the signal (compensated from attenuation effects only) and the curve was computed, obtaining the fully compensated RF signal. Their envelopes were calculated using the Hilbert transform. Afterwards, a two-dimensional (2-D) frame was reconstructed wherein the horizontal and vertical axes corresponded to the acquisition path and the tissue depth respectively. From the resultant image frame, a rectangular region-of-interest (ROI) was selected, containing the neoplasm region for BCC samples and the dermal collagen network for NT samples (excluding evident cutaneous annexes or epidermal parts). The image ROI was sampled to reduce the correlation between adjacent pixels, giving a minimum spacing of half of the lateral resolution (approximately 100/2 mm) between pixels in the horizontal direction and of half of the axial resolution (approximately 30/2 mm) between those in the vertical direction. This sampled ROI ($800 pixels in size) was finally used to compute the statistical parameters. The user interfaces employed for all the mentioned procedures were developed with LabVIEW 7.0 (National Instruments Corporation) software. Computation of statistical parameters The SNR, and the Weibull and GG PDF parameters were computed and analyzed. The SNR was directly computed from the amplitudes of the signals’ envelope. The PDF parameters were obtained from the corresponding curves adjusted to a histogram constructed from the amplitudes. The PDF models’ selection was made based on the results reported by other authors (Raju and Srinivasan 2002). The SNR was obtained from eqn (1): hRi SNR 5 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 hR2 i2hRi
(1)
where R represents the amplitude of the signals’ envelope and the symbol h,i represents the mean value. When the medium meets the Rayleigh conditions, the SNR is close to 1.91. When the SNR is either lower or higher than 1.91, the medium displays pre- or post-Rayleigh characteristics respectively (Raju et al. 2003). The Weibull PDF is defined by eqn (2): bW bW 21 2 R bW R pðRÞ 5 , ,e aW for aw and bw .0: aW aW (2) Here aW and bW represent the PDF amplitude and shape respectively. These parameters were computed by the maximum likelihood method (ML), from the loglikelihood of the function given in eqn (2) (Raju et al. 2003):
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LðR1 ; :::; RN ; aW ; bW Þ 5 N,lnðbW Þ1ðbW 21Þ,
N X i51
where N is the number of pixels compounding the ROI. The expression given in eqn (3) was then derived in terms of aW and equated to zero, obtaining: !1=bw PN bw i 5 1 Ri aW 5 : (4) N The aW value for each arbitrary bW value was obtained from eqn (4). The pair (aW, bW) that maximizes eqn (3), which is the one that best fits the PDF to the histogram shape, was selected to represent the medium. In the Weibull model, when bW is approximately 2, the medium presents Rayleigh characteristics. When bW is 0–2, the medium presents pre-Rayleigh characteristics. When bW is .2, the medium presents Rician characteristics. The PDF of the GG model is defined as (Raju and Srinivasan 2002): yGG R cGG ,RðcGG ,yGG 21Þ 2 aGG pðRÞ 5 ðcGG ,yGG Þ ,e (5) aGG ,GðyGG Þ for aGG ; yGG and cGG .0; where G is the gamma function, aGG represents the amplitude parameter and yGG and cGG represent the shape parameters for the right and left PDF tails, almost independently. In this model, the parameters were also computed using the ML method (Raju et al. 2003), the log-likelihood function being defined by:
Ri 2
b N X Ri W 2N,bW ,lnðaW Þ; aW i51
PN i51
aGG 5
N,yGG
yGG 5 2 cGG > > :
PN
i 5 1 lnðRi Þ 2 N
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!> PN > cGG = i 5 1 Ri ,lnðRi Þ PN cGG > > ; i 5 1 Ri
(7)
(8)
RESULTS The parameters chosen to be analyzed were those related to PDF curve shape (i.e., SNR, bW, cGG and yGG), ignoring the ones related to PDF curve amplitude. This selection was made because some system conditions could cause variations in signal levels for acquisitions made on different days, thus, compromising the comparison of these parameters between samples. The values obtained for all samples of each tissue group (BCC1, BCC2 and NT) and their mean values are shown in Figures 4–7. All tissue groups displayed preRayleigh characteristics, with SNR values of ,1.91 (Fig. 4). The lowest mean value was obtained for the
N P i51
lnðRi Þ2
2N,cGG ,yGG ,lnðaGG Þ2N,lnðGðyGG ÞÞ
8 > > <
!1=cGG
Finally, the set (aGG, yGG, cGG) that maximized eqn (6) (i.e., the best adjustment to the histogram profile) was selected to represent the medium characteristics. In the GG model, when yGG 5 1 and cGG 5 2, the medium presents Rayleigh characteristics. In the particular case where yGG 5 1, the GG PDF is equivalent to the Weibull PDF. The computational program for statistical parameter calculus was developed using the LabVIEW 7.0 (National Instruments Corporation) software. Examples of histogram adjustment with the Weibull and GG curves are shown in Figure 3.
LðR1 ; .; RN ; aGG ; yGG ; cGG Þ 5 N,lnðcGG Þ1ðcGG ,yGG 21Þ,
In this case, the cGG parameter was varied arbitrarily and the corresponding yGG and cGG values were obtained from eqns (7) and (8) respectively:
Rci GG
(3)
N P
i51
Ri aGG
cGG (6)
BCC1 group, whereas the BCC2 and NT groups displayed higher and more similar value ranges. As shown in Figures 5 and 6, the bW and cGG parameters followed the tendencies observed for SNR. The yGG followed an opposite tendency (Fig. 7), being the highest mean value obtained for the BCC1 group. When differences between the groups were analyzed using the Kruskal Wallis test (applicable to a small number of samples) with a 95% level of confidence, significant differences were found for all the studied parameters.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
Fig. 3. Histogram obtained from one selected region of interest (ROI) and the adjusted Weibull PDF (continuous line) and GG PDF (dotted line) obtained by the maximum likelihood method. PDF 5 probability density function; GG 5 generalized gamma.
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Fig. 5. The bW values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
In the present work, the characteristics (concentrations, cross-sections and distributions) of acoustic scatterers present on non-tumoral skin and cutaneous carcinomas (specifically BCCs) were analyzed using a statistical approach. All tissue samples showed pre-Rayleigh characteristics (SNR , 1.91). Significant differences between tissue groups were observed for all the studied parameters. The SNR mean value observed for the BCC1 group was noticeably lower than that obtained for the NT group; no important differences were observed between the NT and BCC2 groups. The lower SNR values and the more obvious pre-Rayleigh characteristics of the BCC1 group can be explained as follows. The scatterers’ reduced concentration was produced by the abundant amorphous stroma surrounding the tumor nests, as well as by the presence of inflammatory cells in the affected region, which are smaller than keratinocytes. Nonhomogeneous scatterer cross-sections were produced because the tumor nests
could not be well distinguished from the surrounding components when the ROI was defined, thus, leading to different scatterer types within it. Conversely, the tumor nests were more easily delimited in the BCC2 group. In this group, the nests primarily contained one scatterer type (atypical keratinocytes), with a few amorphous stroma inside the ROI. In the NT group, the ROI scatterers primarily consisted in a dense collagen fiber network. Similar observations were seen for bW and cGG, while yGG followed an opposite tendency because of the same histologic characteristics. Analogous works conducted with healthy skin have also shown pre-Rayleigh characteristics, although the parameters tended to be more pre-Rayleigh–like than our results (Table 3). These differences are perhaps owing to variations in the methodology employed (e.g., lower frequency levels, differences between in vivo and ex vivo tissue conditions, the body region from where the signal acquisitions were made, etc.). To our knowledge, based on an extensive literature review, no study has been found regarding statistical parameter analysis for cutaneous carcinomas; therefore, those results could not be compared with literature values.
Fig. 4. SNR values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
Fig. 6. The cGG values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
DISCUSSION AND CONCLUSION
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Fig. 7. The yGG values and their means (black markers) obtained from the BCC1, BCC2 and NT groups. BCC 5 basal cell carcinoma; NT 5 non-tumoral.
A typical diagnostic procedure starts with a clinical examination, after which some lesion possibilities are discarded and other considered, as commonly occurs for BCC cases. If the procedure is followed by a UBM imaging, additional differentiation will be possible on diagnostic process, since some BCC characteristics are identifiable by this technique (Petrella et al. 2010a). Further studies can still be necessary after direct and ultrasonic visualization; thus, a quantitative statistical approach was proposed here as a complementary tool. The statistical differences obtained for BCC1 group are important, since the presence of tumor nests are less evident on UBM images (compared with BCC2 group), being primordial additional quantitative procedures. We encompass the analysis to the comparison of BCC cases with healthy skin and actinic keratosis, to first evaluate the method usefulness. Once the results show relevance, other lesions (whose clinical aspects and UBM image characteristics can be similar to that found on BCCs) must necessarily be considered, to give stronger diagnostic significance to the findings. In this sense, because a larger number of patients must be considered, in vivo studies will be more feasible in future. Ex vivo tissue samples were used in this work because of the reasons that follow. First, the UBM system was not designed for human in vivo studies. Second, a large volume of image and signals was acquired, resulting in a long procedure (of few hours), which was impracticable for studies with patients. Finally, ex vivo
Volume 38, Number 10, 2012
measurements allow a better control over some experimental variables (like tissue movements). For applying the method on further noninvasive analysis of cutaneous carcinomas, the results here observed must be compared with in vivo studies. In a first approach it is reasonable to suppose that the differences of parameters values, obtained for ex vivo carcinomatous and not tumoral skin, should also appear for in vivo observations. This is because the scatterers composition, sizes and distribution, which define the statistical properties of ultrasonic signals, are reasonably maintained when the excised tissue is preserved appropriately. Moreover, other kinds of quantitative parameters can be analyzed together with the statistical ones to improve tissue characterization. One approach is the study of acoustic parameters like attenuation, speed or backscattering of acoustic waves that has already been conducted by us. We previously computed acoustic parameters following the method proposed by Ye et al. (1995), which employs signals backscattered along all tissue depth (in our case epidermis, dermis and hypodermis), including parts affected by tumor or not. We did not find significant differences between tissue groups, probably because the acoustic properties from the tumor itself had been obscured by adjacent tissue influence. Nevertheless, other methods to calculate acoustic parameters can be considered in the future. On the other side, Petrella et al. (2010b) made a study of the integrated backscatter coefficient, which represents the backscattered power normalized to the incident intensity as a function of tissue depth. This study was restricted to BCC types whose characteristics are more similar between patients (superficial BCC and Bowen disease), obtaining optimistic results. In the future, the analysis should be extended to more complex BCC types. In addition, the analysis of texture characteristics obtained from ultrasound image processing methods are presently being developed based on works applied to other medical areas (Zhan and Shen 2006). In the near future, we expect to apply spectral analysis to provide information about the scatterers’ shapes (Lizzi et al. 1996). In conclusion, the statistical parameters were able to differentiate one carcinomatous skin condition from
Table 3. Comparison of parameter values obtained in different works for healthy skin Author
fc [MHz]
SNR*
bW*
cGG*
yGG*
Body region
Study
Raju and Srinivasan 2002 Raju et al. 2003 This work**
28 33 45
1.4 1.36 1.72
1.48 1.42 1.79
0.85 0.94 1.44
3.03 2.34 1.55
Fingertip Forearms Arm and forearm
In vivo In vivo Ex vivo
bW 5 Weibull; cGG 5 generalized gamma; yGG 5 generalized gamma; SNR 5 signal-to-noise ratio. * Mean values. ** Considering four healthy skin samples studied.
Statistical analysis of ultrasonic signals from basal-cell carcinomas d L. I. PETRELLA et al.
non-tumoral tissue, presenting reduced scatterers’ concentration with nonhomogeneous cross-sections. We predict that this capability could be used for diagnostic purposes in many cutaneous lesions presenting different scatterers’ patterns. Acknowledgments—The authors are grateful for the financial support provided by CAPES/PROEX, CNPq and FAPERJ, and for the collaboration of staff in the Dermatology and Pathologic Anatomy Departments of HUGG.
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