Learning of speckle statistics for in vivo and noninvasive characterization of cutaneous wound regions using laser speckle contrast imaging

Microvascular Research 107 (2016) 6–16

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Learning of speckle statistics for in vivo and noninvasive characterization of cutaneous wound regions using laser speckle contrast imaging Kausik Basak a,⁎, Goutam Dey b, Manjunatha Mahadevappa b, Mahitosh Mandal b, Debdoot Sheet c, Pranab Kumar Dutta c a b c

Electrical and Electronics Engineering Department, Mahindra Ecole Centrale, Hyderabad 500043, India School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India

a r t i c l e

i n f o

Article history: Received 1 July 2015 Revised 4 April 2016 Accepted 24 April 2016 Available online 27 April 2016 Keywords: Laser speckle Tissue perfusion Support vector machine Wavelet Cutaneous wound

a b s t r a c t Laser speckle contrast imaging (LSCI) provides a noninvasive and cost effective solution for in vivo monitoring of blood flow. So far, most of the researches consider changes in speckle pattern (i.e. correlation time of speckle intensity fluctuation), account for relative change in blood flow during abnormal conditions. This paper introduces an application of LSCI for monitoring wound progression and characterization of cutaneous wound regions on mice model. Speckle images are captured on a tumor wound region at mice leg in periodic interval. Initially, raw speckle images are converted to their corresponding contrast images. Functional characterization begins with first segmenting the affected area using k-means clustering, taking wavelet energies in a local region as feature set. In the next stage, different regions in wound bed are clustered based on progressive and non-progressive nature of tissue properties. Changes in contrast due to heterogeneity in tissue structure and functionality are modeled using LSCI speckle statistics. Final characterization is achieved through supervised learning of these speckle statistics using support vector machine. On cross evaluation with mice model experiment, the proposed approach classifies the progressive and non-progressive wound regions with an average sensitivity of 96.18%, 97.62% and average specificity of 97.24%, 96.42% respectively. The clinical information yield with this approach is validated with the conventional immunohistochemistry result of wound to justify the ability of LSCI for in vivo, noninvasive and periodic assessment of wounds. © 2016 Elsevier Inc. All rights reserved.

Introduction Skin serves as a multilayered protective shield against external environment to maintain regular homeostasis. Any loss of its functional integrity accounts for acute or chronic diseases is clinically known as cutaneous wound. Acute wounds heal through three stage healing procedure — inflammation, tissue formation and tissue remodeling. In contrast to this, chronic wounds (ischemic ulcers, diabetic ulcers, etc.) experience abnormal progression and are also referred to as nonhealing wounds (Gefen, 2012; Singer and Clark, 1999). Tumor wound falls into the second category. During its prolonged evolution process, study of gradual changes in tissue characteristics (due to change in cellular response) and blood perfusion helps to narrow down the effectiveness of different treatment procedures. As a prior art of study, clinicians undergo detailed examination of a wound bed before finalizing possible and accurate treatment strategies.

⁎ Corresponding author at: Mahindra Ecole Centrale, Survey No. 62/1A, Bahadurpally, Jeedimetla, Hyderabad 500043, Telangana, India. E-mail address: [email protected] (K. Basak).

http://dx.doi.org/10.1016/j.mvr.2016.04.008 0026-2862/© 2016 Elsevier Inc. All rights reserved.

According to a statistical survey in India, cutaneous wounds have an incidence of 15 per 1000, in which 10.5 corresponds to acute cases while 4.5 are chronic. In global scenario, it can be observed in 30% of infants and adolescents, residing in rural and urban-slums in low and middleincome countries (LMIC) (Gupta et al., 2004; Joseph et al., 2014; Ahmed et al., 2013; Karthikeyan et al., 2004; Hay et al., 2006). That is why it is becoming a public health concern due to (a) unawareness about their severity and (b) lack of understanding in wound characterization, leading to a misjudgment towards proper clinical assessment. Over the past decade significant research has been undertaken for the clinical assessment of cutaneous wound, aiming to a patient's comfort centric management system. Periodic monitoring of cutaneous wound region is of clinical importance to study the dynamic behavior of wound. Chronic wounds undergo abnormal progression, in which gradual changes in tissue characteristics and vascular heterogeneity vary from healing wounds. Assessment of such prolonged evolution process depends on prior sense and experience of the clinician (Gefen, 2012). In general, clinicians undergo an invasive methodology that involves biopsy of the wound tissue, followed by its biochemical processing and histological depiction for standardized clinical reporting. Although it quantifies the tissue involvement in a wound from its surface to its

K. Basak et al. / Microvascular Research 107 (2016) 6–16

depth, it lacks in the context of being an invasive, ex vivo and time consuming procedure. These challenges have encouraged the inception of subsurface imaging techniques for in vivo, non-invasive and real-time monitoring of wound progression. In this context, LSCI provides a low cost solution with high resolution probing facility for in vivo and noninvasive assessment of cutaneous wound. In biological media, LSCI helps to map a relative velocity of the moving cells (red blood cells), discriminating higher and lower blood flow regions. The imaging physics is based on the statistical analysis of speckle pattern, formed due to constructive and destructive interferences of backscattered lights at the imaging plane (Basak et al., 2012; Briers et al., 2013). Nearly all researches in LSCI are focused on relative mapping of blood flow on human forearm (Richards and Briers, 1997), human skin capillaries (Fujii et al., 1987), retinal and brain circulation of animal models (Ponticorvo et al., 2013; Fujii et al., 2007; Dunn, 2011; Miao et al., 2010), burn injury (Zimnyakov and Misnin, 2001) and characterizing atherosclerotic plaques (Nadkarni et al., 2005). This study extends the application of LSCI for wound characterization in mice model. Previously, Kruijt et al. examined vasculature response in tumor affected veins and arteries on a rat skin-fold model using LSCI (Kruijt et al., 2006). The research was based on the fact that optical properties were changed in tumor affected veins and arties, leading to a change in speckle pattern and perfusion. Also, a study of microvascular remodeling and hemodynamic changes during wound healing angiogenesis was performed using LSCI (Rege et al., 2012). This work focuses on the study of a tumor wound using LSCI for monitoring its progression, change in tissue structure and characterization of different regions. Initially, captured raw speckle images are converted to contrast images to increase the spatiotemporal resolution. Characterization of wound regions includes first segmentation of wound area, followed by classification of progressive and nonprogressive wound regions. At first stage, wavelet energies are computed inside a local region of the contrast image. These energy values are used as feature set for segmentation of the wound area using kmeans clustering algorithm. Different regions within the wound area are further classified using supervised learning (support vector machine) of speckle statistics in a local region of the image. Clinical information yielded from this approach is validated with respect to immunohistochemistry analysis of the wound tissue to study the changes in functional and morphological characteristics during tumor wound progression. Materials and methods Statistical physics of LSCI Speckle pattern is formed when an optically rough surface is illuminated by a laser and the backscattered lights interfere with each other, producing a two dimensional image of the optical scattering media. Speckle contrast C is usually defined as the ratio of spatial standard deviation σ to mean intensity 〈I〉 of speckle pattern. Spatial variance of intensity (σ2) is a function of time average of autocovariance of intensity fluctuations (Ct(τ)) (Fercher and Briers, 1981). Assuming stationarity, normalized autocorrelation of intensity fluctuations can be written as gt(τ) = 1 + ct(τ) where, normalized autocovariance is defined by ct(τ) = Ct(τ)/〈I〉2t (Fercher and Briers, 1981). As the autocorrelation of speckles depends on underlying velocity distribution of scattering particles, so the autocovariance function also depends on the same. Considering a Lorentzian velocity profile of moving scatterers, C can be derived as a function of the ratio of correlation time to exposure time (τc/T) (Briers et al., 2013), 

 1 =2 τc τc 2 2T −1 þ exp − C¼ β T 2T 2 τc

ð1Þ

7

where, β corresponds to loss of correlation related to the loss of detector pixel size and speckle size. A relative flow can be estimated from τc over predetermined time duration. The relation between τc and relative flow v is given by Basak et al. (2012) v¼

λ 2πτc

ð2Þ

where, λ is the laser wavelength. Change in contrast due to change in correlation time of speckle fluctuations can be derived from Eq. (1) as 

C dC 1  ¼ exp −2 x −1 þ dx 2C x

ð3Þ

where, x¼τc =T . Any change in flow can be evident from change in contrast values at flow regions. The sensitivity of change in speckle contrast for absolute and relative change in flow can be estimated using Eqs. (1) and (3). Absolute sensitivity(Sabs)and relative sensitivity(Sabs)can be derived as Sabs ¼

dC 2πτc dC :x: ¼− λ dv dx

dC x dC Srel ¼ C ¼− : : dv C dx v

ð4Þ

ð5Þ

Theoretical plots for contrast, absolute sensitivity and relative sensitivity as a function of the ratio x ¼ τc =T are given in Fig.1. These curves are normalized with respect to their scales. These characteristic plots reflect a strong dependency between contrast value and correlation time (or equivalently velocity) of the scatterers. With increase in v, correlation time τc decreases and correspondingly x decreases. Smaller correlation time relates to higher flow regions, showing a reduced contrast value; whereas, for particles with higher correlation time, speckle pattern remains fully developed (Fig.1a). For single exposure, T is constant and both sensitivities vary with τc. Sensitivity of speckle contrast to absolute flow changes is maximum at τc ≈ 0.05T(Fig. 1b). For τc b 0.05T, Sabs increases proportionally with correlation time; but forτc N 0.05T, Sabsmaintains an inverse relationship with τc. On the other hand, Srel decreases from its highest value (0.5) with increasing value of τc (Fig. 1c). After τc ≈ 0.05T, it decays drastically with increase value of correlation time. However, these plots highly depend on the underlying velocity distribution of scattering particles. A Lorentzian distribution is assumed for deriving the equations. Any change in velocity distribution, will significantly change the shape of these curves. Experimental methodology In vivo imaging of wound using LSCI As depicted in Fig. 2(a), a diode laser source (Thorlabs Inc., USA, 675 nm, 3.5 mW) is tuned to illuminate the wound region of the animal. The fiber-pigtailed laser (LPS-675-FC, Thorlabs Inc., USA) is connected to a laser diode driver (LM9LP, Thorlabs Inc., USA) and the current is controlled by a laser diode controller (TLD001, Thorlabs Inc., USA). A monochrome 12-bit CCD camera (SCA640-70fm, Baslar Scout, Germany) is positioned with a macro imaging lens and polarizer assembly to capture speckle images. The imaging lens is placed at a distance of approximately 30 mm from the sample. Polarizer angle is kept at 40° to reduce specular reflections from surface layer. The illuminated region is captured with 480 × 640 pixels, yielding an area of approximately 4 mm × 5 mm at 1× magnification of the lens. Speckle images are acquired at a rate of 50 frames per second. There are certain imaging parameters that have to be adjusted during LSCI: (a) number of pixels for calculating contrast, (b) typical speckle size, and (c) exposure time of CCD camera. With too larger pixels, the spatial resolution of the image will reduce, whereas more statistical

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Fig. 1. Characteristic plots for (a) contrast, (b) absolute sensitivity and (c) relative sensitivity as a function of x ¼ τc =T . The sensitivity plots are normalized for better theoretical realization of the relation between change in contrast and correlation time (hence, the measured flow).

noise will be incorporated into the computation if number of pixel is too small. In general, a square of 5 × 5 or 7 × 7 pixels is used to maintain this trade-off. On the other hand, the speckle size depends on the camera system. The size of the speckle can be determined by d = 1.2(1 + M)λF where, M is magnification of the imaging lens, λ. is wavelength of the laser light and F is camera F-stop number. Theoretically, to obtain good statistics and lesser error, the speckle size has to be same with the pixel size of speckle image. This can be achieved by properly adjusting the lens focus and the camera F-stop number. For a diode laser source (λ = 675 nm) and camera pixel size of 7.4 μm × 7.4 μm, the optimal F-stop number comes out nearly 4.56. In practice, camera F-stop is kept at ≈4.

Optimal camera exposure time is obtained with different LSCI trials. Dependencies of both relative sensitivity and relative noise for different exposure time are plotted in Fig. 2(b, c). It is apparent from Fig. 2(b) that with increase in camera exposure time relative sensitivity increases sharply initially; but it reaches to a plateau once exposure time crosses ≈ 10 ms. On the other hand, relative noise is account for physiologic disturbances, statistical errors in contrast computation and often hardware related noise. Increment of relative noise is very low in lower exposure times, but after exposure time N 10 ms, it increases drastically (Fig. 2c). Therefore, an exposure time of nearly 10 ms can give satisfactory result of speckle contrast with higher sensitivity and less amount of noise.

Fig. 2. (a) Schematic diagram of overall proposed approach including LSCI imaging set-up. (b) Experimentally measured camera exposure time versus normalized relative sensitivity and (c) relative noise plots. Mean values for both relative sensitivity and relative noise over several trials for an exposure time are plotted. Relative sensitivity is normalized with respect to the maximum value over all exposure times.

K. Basak et al. / Microvascular Research 107 (2016) 6–16

In vivo speckle imaging of the tumor wound was carried out on male Swiss albino mice (approximate age 7 weeks and weight 25 g, N = 10). The mice were fed a standard pellet diet and animal study was performed under the Guidelines of the Institutional Animal Ethical Committee (Indian Institute of Technology Kharagpur). Sarcoma 180 cells were taken at logarithmic phase and suspended in sterile phosphate buffer saline (PBS) at density of approximately 2 × 106 cells in 0.2 ml for each mouse. Tumor cells (Sarcoma 180) was injected subcutaneously at right flank of each mouse. The mice were maintained in an animal house under pathogen-free environment and standard temperature (32 ± 2 °C) and humidity (60 ± 5%) with alternating 12hour light/dark cycles. After 10 days from the day of injection, prominent solid tumors with approximate volume of 90–100 mm3 were observed. A wound (reddish brown color) was found to develop on the cutaneous layer of solid tumors after 15 days and LSCI study was performed on those wound regions. In the following days, a gradual progression of wound regions was observed and on day 20, another LSCI study was conducted. The animals were anesthetized with chloroform (5 ml in vapor state) during optical probing mechanism. The anesthetized animals were placed on a stereotaxic platform to reduce the movement of animal body. The laser diode was placed at an angle nearly 30° with a horizontal axis. A refractive index matching gel was applied on the wound region to reduce signal loss at air-epithelium junction, leading to an increased penetration depth. Biological parameters like heart rate, blood pressure and oxygen (O2) saturation level were monitored continuously on a regular interval to keep them in normal physiological ranges. The

9

in vivo study of the cutaneous wound using laser speckle imaging system was carried out inside an air-conditioned room where temperature was maintained around 25 °C. However, as this LSCI study is a part of main stream work focused on cancer research, the mice were kept in an animal house where ambient temperature used to vary between 30 and 35 °C. Besides, as was felt by touching the mice body prior to the in vivo experiment (within a controlled temperature environment), it can be said that the mice did not have fever or any abrupt elevation in body temperature. Post to the in vivo experiment, animals were kept in normal air to maintain their regular homeostasis. Wound tissue labeling using CD31 expression of immunohistochemistry Post LSCI acquisition, immunohistochemistry of wound tissue was performed according to the earlier reported method by the cancer biology group of School of Medical Science and Technology, IIT Kharagpur (Das et al., 2012). The expression profile of CD31 protein (an angiogenesis marker that provides density of blood vessels in wound area) of the wound tissue sample was studied at different regions of the wound at day 20 of wound progression. Briefly, wound tissue samples were fixed in formalin, dehydrated and embedded in paraffin. Thin sections (approximately 3–4 mm) of wound tissue were prepared using a microtome and immunohistochemical analysis was performed within 24 h. The tissue sections were then deparaffinized in xylene and rehydrated with graded alcohol (100%, 95% and 80% v/v). Later the sections were washed in distilled water. After antigen retrieval, the sections were incubated in 5% BSA, followed by appropriate primary and secondary antibodies. After washing, sections were exposed to avidin–biotin peroxidase complexes

Fig. 3. (a) Results of Immunohistochemistry using CD31 marker of different samples taken from both slender and wider region of the wound. Samples from regions R1, R2 and R3 show low CD31 expression, describing the low density of blood vessels at wider regions. On the other hand, samples from regions R4, R5 and R6 reflect high CD31 expression for more vascular density at slender regions of the wound. (b) Digital image of the wound region at day 20, (c) its corresponding contrast image and (d) annotated tissue labels of wound region using immunohistochemistry results. Higher density of blood vessels (progressive nature of the wound) is marked in red color. Lower vascular density (non-progressive nature of the wound region) is marked in green color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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for 30 min. Diaminobenzidine (DAB) was added as chromogen. Meyer's hematoxylin was used to counterstain the wound tissue sections. Finally, they were dehydrated and mounted with epoxidic medium. Microscopic images (Leica Microscopes, Germany) were captured with objective magnification of 20× for immunohistochemical study. Results of CD31 expression profile are shown in Fig. 3(a). It can be observed that the expression of CD31 is low at wider regions (sharp margins) of the wound (R1, R2 and R3 in Fig. 3a), describing the low density of blood vessels at those regions. In contrast to this, the expression profile is high at slender regions of the wound area with relatively rough margins, resembling high density of blood vessels at regions R4, R5 and R6 in Fig. 3(a). Higher vascular density at slender regions corresponds to the higher blood flow and progression of wound in that direction. Since, it requires more supply of nutrition and oxygen, higher blood flow in those regions is necessary. On the other hand, low vascular density with low blood flow at wider regions depicts its nonprogressive nature. Fig. 3(b) is the digital photograph of the tumor wound region at day 20. Its corresponding contrast image is depicted in Fig. 3(c). It provides a variation in local speckle statistics in terms of pixel intensities corresponding to the disparity in vascular distribution. The progressive and non-progressive regions of wound are annotated (Fig. 3d) by an expert group of researchers based on immunohistochemical analysis and histopathological study of the wound bed. The immunohistochemistry of wound tissue is performed by the cancer biology group of School of Medical Science and Technology, IIT Kharagpur (Das et al., 2012). As a part of the main work, this study is performed in light of the LSCI imaging, where the main work is focused on the study related to cancer research. The expression profile of CD31 protein of the wound tissue sample, taken from various regions of the wound bed, depicts the vasculature density as well as relative blood perfusion at those regions. This is also apparent from the spatial distribution of speckles as it varies at different regions of the wound bed. From the results of immunohistochemical study, higher vascular density is observed mostly in the slender region of the wound, whereas lower vascular density is found in the tissue samples taken from wider region of the wound. Such findings over a large amount of samples help the experts to annotate different regions of the wound bed. Higher density of blood vessels, describing progressive nature of the wound, is marked in red color. Lower vascular density (denoting non-progressive nature of the wound region) is marked in green color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Functional characterization of wound regions The overall processing steps are described in Fig. 2(a). Initially, all raw speckle images are transformed to contrast images using our earlier published work: adaptive computation of contrast technique (acronym adLASCA) (Basak et al., 2014). The procedure includes first registration of the raw speckle images, followed by adaptive contrast computation based on speckle statistics within a local region of the image. Initially, registration is performed to compensate for motion artifacts due to respiration and heart beating of animal. adLASCA helps to reduce the effect of corrupting speckles, maintaining integrity of dynamic speckle pattern in successive time frames. It also increases the spatiotemporal resolution of speckle images. Details of the algorithm and its implementation, parameter controlling, accuracy and validation are discussed in Basak et al. (2014). Functional characterization begins with first segmentation of the affected area using k-means clustering, taking wavelet energies for each pixel as feature set. In the next stage, different regions of wound area are classified using supervised learning of the speckle statistics, computed within a window region of different dimensions. Support vector machine is implemented with a polynomial kernel of order 3 for characterization of progressive and non-progressive regions of wound. The detailed computational approach is discussed below. Segmentation of wound area Speckle contrast images (with locally varying statistics) comprise different combination of abrupt features with contrasting homogeneous regions. Fig. 4(a) comprises an adLASCA image of the tumor wound and the histograms of the intensity profiles at different selected regions (T1– T5). It is evident that speckle statistics are indeed different for different regions. Such distinctive response of speckles for cutaneous wound regions is estimated using level 2 wavelet decomposition (Gonzalez, 2014). Multiresolution analysis focuses on the local statistical variations of contrast images at different resolution levels. The discrete wavelet transform of the input contrast image f(x, y) of size M × N is computed using −1 X NX 1 M−1 f ðx; yÞφ j0 ;m;n ðx; yÞ W φ ð j0 ; m; nÞ ¼ pffiffiffiffiffiffiffiffi MN x−0 y−0 −1 X NX 1 M−1 f ðx; yÞψij;m;n ðx; yÞ; i ¼ fH; V; Dg W iψ ð j; m; nÞ ¼ pffiffiffiffiffiffiffiffi MN x−0 y−0

ð6Þ

Fig. 4. (a) adLASCA image of the wound region and histogram plots for different regions (T1–T5) selected over the contrast image, (b) its corresponding wavelet decomposed image at level 2 resolution, (c) equivalent wavelet energies are computed for different approximations.

K. Basak et al. / Microvascular Research 107 (2016) 6–16

where, j0 is an arbitrary starting scale. Wφ(j0, m, n) is approximation coefficient and Wiψ(j, m, n) refers to coefficients of horizontal, vertical and diagonal details for scales j0 ≤ j. The scaled and translated basis functions are defined as   j φ j;m;n ðx; yÞ ¼ 2 =2 φ 2 j x−m; 2 j y−n

ð7Þ

  j ψij ;m;n ðx; yÞ ¼ 2 =2 ψ 2 j x−m; 2 j y−n

ð8Þ

where, i = {H,V,D} denotes horizontal, vertical and diagonal representations. Level 2 wavelet decomposition of the wound contrast image is shown in Fig.4(b). H1, V1 and D1 represent the horizontal, vertical and diagonal coefficients respectively for level 1 decomposition. Also A2, H2, V2 and D2 stand for approximation, horizontal, vertical and diagonal coefficients for level 2 wavelet decomposition of the wound image. Energy values are computed from these coefficients for each pixel within a local region of the adLASCA image. Fig. 4(c) shows the energy values corresponding to the different wavelet coefficients (Fig. 4b). These energy values are further used as feature set for k-means clustering technique (Mitchell, 1997). k Value is kept as 2 for the segmentation of normal and affected regions. Based on the feature set, a pixel is assigned to a particular cluster using a Euclidian distance measure: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n uX ðxi −yi Þ2 dðxi ; yi Þ ¼ t

11

the pixels in segmented area. Output class yi corresponds to binary class {progressive, non-progressive} regions. Cross-plot for the input training data reflects non-linearly spaced feature vectors, for which a polynomial kernel function k(xi,xj) = (xi,xj + 1)d of order 3 is used to classify the regions within wound bed. The problem aims to maximizes the distance between hyper-plane and support vectors with a constraint, yi(w − Φ(xi) − b) ≥ 1 where, Φ(xi) is a non-linear mapping of the input training dataset into feature space where the training set becomes linearly separable and k(xi,xj) = Φ(xi) Φ(xj). The constraint optimization problem leads to the primal form of the classification: ( ) X 1 2 α i ½yi ðw  Φðxi Þ−bÞ−1 : arg min max kwk − 2 w;b α≥0 i

ð10Þ

It can be solved by standard quadratic programming technique with w ¼ ∑i α i yi Φðxi Þ. The dual formulation can be represented as minD ðα Þ ¼ α

XX

1X αi − α i α j yi y j k xi  x j : 2 i i j

ð11Þ

This allows the algorithm to fit in maximum-margin hyper-plane in a transformed feature space. For a testing data z, (w ⋅ Φ(z) + b is computed and classify z as class 1 if sum is positive, and class 2 otherwise. Results and discussion

ð9Þ

i¼1

where, xi is i-th sample, μk is centre of k-th cluster and n is total number of pixels. The affected region of tissue is segmented by iterative partitioning over the clusters to minimize the sum of pixel-to-centroid distances, summed over the two clusters. Characterization of wound regions Wound regions are characterized by support vector machine (SVM) where speckles corresponding to specific tissue area are marked by an expert annotator. SVM is a single layer and non-linear network which maximizes the distance between the class and separating hyper-plane (Mitchell, 1997). Feature set includes speckle statistics like entropy, variance, kurtosis (each is measured in 3 × 3, 5 × 5, 9 × 9, 13 × 13 windows) and local binary pattern (LBP) (Mitchell, 1997). The discriminating potential of the features to characterize the labeled classes (progressive and non-progressive regions) is measured with respect to independent sample t-test. It is implemented to compare the population mean of two classes when population mean and variance are unknown. The test assumes that the population is normally distributed with zero mean and unknown variance. However for non-normal population (having large sample), the test also performs well. The feature statistics of the dataset is shown in Table 1. The procedure also gives the confidence interval for the difference of means. A large difference between the means leads to the rejection of null hypothesis, H0 : μ1 = μ2. Results show that the test rejects the null hypothesis at a significance level α = 0.05. p-Values for all statistical features fall below α = 0.05 and 95% confidence interval of the mean do not contain zero. Feature set for the wound region can be represented as x = x1 , x2 , . . . , xn where xi is a column vector, representing a feature for

Table 1 p-Value for different statistical features. Window

Entropy

Variance

Kurtosis

LBP

3×3 5×5 9×9 11 × 11

0.042 0.021 0.016 0.037

0.039 0.027 0.019 0.024

0.047 0.031 0.023 0.028

0.029

A periodic study is carried out at a regular interval of 5 days to observe wound progression. Experimental analysis is performed over 84 LSCI scans for different days of image set. Fig. 5(a, b) shows raw speckle image and its corresponding adLASCA image of the cutaneous wound region at day 20. adLASCA substantially improves the contrast between different characteristic tissue structures with an increased spatiotemporal resolution. Fig. 5(c) corresponds to the digital photograph of the wound region. Speckle images are acquired at 50 fps for 30 s in each experiment. All image analysis operations are implemented using MATLAB (R2012b, Mathworks, MA, USA) on a 2.53 GHz Intel(R) Core(TM) i5 CPU. Relative blood perfusion and clinical relevance The tumor wound bed is heterogeneous with respect to its vascular distribution. LSCI study helps to depict the variation in local statistics in terms of pixel intensities, which essentially corresponds to the disparity in vascular distribution. A digital photograph of the tumor wound area and its corresponding adLASCA image are shown in Fig. 6(a) and (b) respectively. Eventually, progression of wound from adLASCA image can be correlated and validated with the findings of immunohistochemistry study (Wound tissue labeling using CD31 expression of immunohistochemistry section). Different regions in adLASCA images are annotated by an expert (Fig. 3d) based on the CD31 expression profile (as described in Wound tissue labeling using CD31 expression of immunohistochemistry section) to study the heterogeneity in functional characteristics within wound bed. In short, higher blood vascularity at slender region of the wound bed corresponds to higher blood perfusion, for which speckle intensity is reduced in adLASCA images at those areas (progressive wound region). On the other hand, the wider region with sharp margin resembles relatively high contrast due to low vascular density and reduced blood perfusion, describing non-progressive nature of the wound. Besides, study of blood perfusion in dense vascular mesh of wound also carries significant information about tissues' functional behavior. Relative blood flow plot (Fig.6d) is estimated from regions in Fig.6(b). It is apparent that blood flow in regions R1, R2 and R3 is higher than the regions R4, R5 and R6. Relative blood perfusion mapping using

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Fig. 5. Tumor wound region at mice right flank. (a) Raw LSCI image, (b) its corresponding adLASCA image and (c) digital photograph of the wound region at day 20.

color coded model (Fig.6c) also substantiates blood flow characteristics in progressive and non-progressive wound regions. At the same time, reduction in blood flow is observed from the reduced color levels at wider or non-progressive area. Higher perfusion in slender region of the wound also justifies its progressive nature. This should be noted that blood perfusion is observed relatively low in regions apart from the wound bed.

Segmentation of wound area Wound region is segmented using k-means clustering (2 clusters) technique. Feature set is computed using wavelet decomposition (level 2) of the image. An 8 × 8 spatial window is used to compute the coefficients for each pixel. Energy values are computed in each subblock and seven energy matrices are formed with dimension M × N.

Fig. 6. (a) Digital photograph with marked regions of the cutaneous wound at day 20. (b) Six regions are selected on adLASCA image of the corresponding tumor wound area to study the blood flow dynamics. (c) Relative blood perfusion mapping using adLASCA technique, showing high and low perfusion areas. (d) Relative blood flow plot at different regions of the adLASCA image.

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These energy values correspond to the local statistical variations due to structural and functional changes in tissue vascularity. Segmentation results using the proposed method for three different mice are depicted in Fig. 7. Contrast images are shown in Fig. 7(a, d, g, j), in which Fig. 7(a) and (d) corresponds to the wound regions for mice M1 at day 15 and day 20 respectively. Also, Fig. 7(g) and (j) is the wound contrast images of mice M4 and mice M10 at day 20, respectively. Fig. 7(b, e, h, k) presents the annotated images of the wound regions for respective days and mice. The last column of Fig. 7 corresponds to the segmentation results using proposed technique. Segmentation of the wound regions of mice M1 at day 15 and day 20 are shown in Fig. 7(c) and (f) respectively, whereas Fig. 7(i) and (l) represents the segmented wound regions for mice M4 and mice M10 at day 20, respectively. Besides, a comparative evaluation is presented in Table 2 based on the segmentation accuracy with respect to other segmentation techniques. The methodologies include: adaptive thresholding based on contrast (Forsyth and Ponce, 2009), anisotropic diffusion (Perona and

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Malik, 1990), and entropy based thresholding (Forsyth and Ponce, 2009). Segmentation accuracy is computed using Eq. (12). A represents the segmented wound region using k-means clustering and B is the ground truth image, marked by a group of expert annotators. Apparently, the proposed algorithm can efficiently segment the wound area, with an average segmentation accuracy of 98.37%. Segmentation Accuracy ¼ 1−

absðA−BÞ  100% B

ð12Þ

Characterization of progressive and non-progressive wound regions Segmented wound regions are further used to characterize progressive and non-progressive areas for clinical assessment about wound progression. A feature set of MN × d is computed using different statistical measures within the segmented area. A 8-fold cross-validation is

Fig. 7. Segmentation results of the wound area using the proposed technique for different days and mice. First column represents the contrast images of the wound in which (a) and (d) are the images of mice M1 at day 15 and day 20 respectively. Similarly, contrast images of wound of (g) mice M4 and (j) mice M10. Second column (b, e, h, k) corresponds to their respective annotated affected regions and the third column (c, f, i, l) holds their respective segmentations of wound region.

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Table 2 Performance evaluation of the proposed approach. Segmentation techniques

Average segmentation accuracy (%)

Adaptive thresholding Mitchell (1997) Anisotropic diffusion Forsyth and Ponce (2009) Entropy based thresholding Mitchell (1997) Proposed approach

79.81 83.63 87.25 98.37

performed using 84 LSCI images. Classification results are compared with their corresponding annotated wound tissue labels achieved using immunohistochemistry study. The classification algorithm is trained with 56 LSCI images and the rest 28 images are used for testing purpose. Results of supervised classification for three different mice at day 20 are shown in Fig. 8. The first column (containing Fig. 8a, d and g) represents annotated tissue labels of the wound bed of mice M1, M4 and M10 respectively. The progressive region is marked in red and relatively non-progressive region is marked in green color. In first row, Fig. 8(b) and (c) corresponds to progressive and non-progressive wound regions (after classification) of mice M1. Similarly, Fig. 8(e, f) shows the posterior probabilities of classification outputs (progressive and non-progressive labels) of the wound area of mice M4 and Fig. 8(h, i) represents progressive and non-progressive wound regions for mice M10. A group of expert medical researchers report the performance of such LSCI based method for wound region classification by

comparative study with digital images and results from immunohistochemistry analysis. Quantitative analysis with respect to annotated tissue labels (using immunohistochemistry results) also indicates that the posterior probabilities are accurate in classifying progressive and non-progressive wound regions. These results are tabulated in Table 3. For training dataset, both sensitivity and specificity are observed very high (average sensitivity of around 97.35% for progressive and 98.16% for nonprogressive wound regions with specificity of 98.6243% and 97.9254% respectively). Classification accuracies for progressive and nonprogressive wound regions over testing dataset are also found significant using the proposed algorithm. About 96.18% of the progressive wound region is accurately labeled in testing images with a specificity of 97.2439%. In case of non-progressive wound region, 97.62% of the wound bed is accurately classified with specificity of 96.4258%. Performance evaluation of the proposed classification technique is carried out based on classification accuracy and receiver operating characteristics (ROC) with respect to different classification algorithms implemented over testing dataset. These include: Bayesian classification (Mitchell, 1997), K-nearest neighbor (K-NN) (Mitchell, 1997) and kmeans clustering (Mitchell, 1997) of cutaneous tissue regions based on speckle contrast. Results are tabulated in Table 4. Apparently, the proposed supervised classification technique (SVM) based on different feature set of speckle images substantiate the efficacy of classifying progressive and non-progressive wound tissue with an average classification accuracy of 97.58% and average ROC of 0.9702.

Fig. 8. (a, b, c). Annotated tissue labels, progressive and non-progressive wound regions for mice M1 at day 20 respectively. The progressive region is marked in red and relatively nonprogressive region is marked in green. Similarly in second row, figures (d, e, f) represent prior information of tissue labels, progressive and non-progressive regions of wound bed for mice M4 at day 20 respectively. In the last row, figures (g, h, i) follow the same order for mice M10 at day 20 respectively.

K. Basak et al. / Microvascular Research 107 (2016) 6–16 Table 3 Classification results over training and testing dataset using proposed approach. adLASCA images Training dataset Testing dataset

Tissue class

Sensitivity (%)

Specificity (%)

Progressive Non-progressive Progressive Non-progressive

97.3529 ± 2.1637 98.1635 ± 1.3942 96.1847 ± 2.0479 97.6227 ± 1.9215

98.6243 ± 1.1438 97.9254 ± 1.8724 97.2439 ± 1.6748 96.4258 ± 2.2417

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why, to reduce computational burden and achieve high classification accuracy, the proposed procedure is followed. Moreover, imaging of other types of cutaneous wounds using LSCI would possibly provide clinically useful information to characterize different regions. It would definitely be interesting to study the blood flow patterns for various other kind of wounds (e.g., healing and non-healing wounds). In future research work, the present study can be extended to perform such exercise in correlation with other imaging modalities for characterization of tissue structures.

Discussion Conclusion LSCI is highly influenced by tissue properties which can be characterized by the received backscattered signals. This paper focuses on monitoring and characterization of tumor wound regions on mice model. It extends the application of LSCI where mostly it is used for relative blood perfusion mapping on retinal and brain circulation, characterization of atherosclerotic plaques, skin capillaries etc. It should be noted that tissue behavior changes significantly in tumor wound as compared to other kinds of cutaneous wounds. Such type of wound possesses angiogenesis for which higher blood flow in progressive region can be related to the tumor growth process. However, the underlying physics of the imaging technique depends on the changes in tissue optical properties due to changes in structural and functional characteristics at cellular level. Prior to classification of the wound region, segmentation of the wound is performed using k-means clustering technique. Although it can approximate the wound region with significant high segmentation accuracy, there are some portions of unaffected cutaneous tissue which are counted as a part of the wound even after segmentation of the wound bed from its surrounding unaffected region. Therefore, straightforward classification of progressive and non-progressive areas within the segmented wound region may include some amount of unaffected cutaneous tissues and this may incur some false classification also. This reason the sensitivity and specificity of the progressive and nonprogressive regions are computed separately, although it is considered as a two class problem. Sensitivity and specificity of progressive region are computed with respect to the non-progressive and unaffected regions within wound bed. Similarly, sensitivity and specificity results of non-progressive region are presented with respect to progressive and unaffected regions within the wound area. Sensitivity of progressive region corresponds to the true positive rate (TPR) at which the wound tissues (showing progressive nature) are classified with respect to non-progressive wound tissues and unaffected tissues. On the other hand, specificity signifies the true negative rate of classifying the non-progressing region and unaffected tissues. In case of non-progressive wound region, sensitivity and specificity are calculated in similar way. Finally, results are presented for both training and testing group of images in Table 3. As per the result, it is apparent that specificity of progressive region and sensitivity of nonprogressive area are closely similar. A small amount of difference lies due to presence of unaffected tissues within segmented wound bed. However the classification procedure can be applied to the whole image (with three classes) irrespective of the segmentation result, but this will increase the computational burden and the learning procedure using support vector machine will also be affected due to this. That is

Table 4 Performance evaluation of the proposed classification technique. Classification techniques

Average accuracy (%)

Average ROC

Bayesian classification Gonzalez (2014) K-NN Gonzalez (2014) k-means Gonzalez (2014) Proposed Technique

90.7519 ± 1.4973 87.4127 ± 1.9264 81.2633 ± 2.3184 97.5824 ± 1.7328

0.9136 0.8717 0.8131 0.9702

This work presents an in vivo, noninvasive, real-time and low cost imaging solution using LSCI for periodic monitoring and characterization of cutaneous wound. The framework is found to accurately track the changes in structural and functional characteristics of tissue within tumor wound region. Speckle pattern changes characteristically with wound progression and a statistical learner (learning these local changes) can approximate the wound regions based on the tissue behavior. Spatial variation of contrast due to functional heterogeneity within wound region is estimated using different statistical measures at local region of each pixel. A support vector machine is modeled using these statistical features and implemented to classify progressive and nonprogressive regions of wound bed. When applied on testing data, the algorithm efficiently characterizes both regions with average sensitivity of 96.18%, 97.62% and specificity of 97.24%, 96.42% (for progressive and non-progressive wound regions). Classifications outputs are also validated with the results of immunohistochemistry analysis. Therefore, the proposed approach using LSCI can be applied for monitoring wound progression, aiming to in vivo and non-invasive assessment of wound characteristics. References Ahmed, A., Leon, A., Butler, D.C., Reichenberg, J., 2013. Quality-of-life effects of common dermatological diseases. Semin. Cutan. Med. Surg. 32 (2), 101–109. Basak, K., Mahadevappa, M., Dutta, P.K., 2012. Review of laser speckle-based analysis in medical imaging. Med. Biol. Eng. Comput. 50 (6), 547–558. Basak, K., Dey, G., Mahadevappa, M., Mandal, M., Dutta, P.K., 2014. In vivo laser speckle imaging by adaptive contrast computation for microvasculature assessment. Opt. Lasers Eng. 62, 87–94. Briers, D., Duncan, D.D., Hirst, E., Kirkpatrick, S.J., Larsson, M., Steenbergen, W., Stromberg, T., 2013. Thompsonc OB laser speckle contrast imaging: theoretical and practical limitations. J. Biomed. Opt. 18 (6) (066018–1). Das, S., Dey, K.K., Dey, G., Pal, I., Majumder, A., MaitiChoudhury, S., Kundu, S.C., Mandal, M., 2012. Antineoplastic and apoptotic potential of traditional medicines thymoquinone and diosgenin in squamous cell carcinoma. PLoS ONE 7 (10), e46641. Dunn, A.K., 2011. Laser speckle contrast imaging of cerbral blood flow. Ann. Biomed. Eng. 40 (2), 367–377. Fercher, A.F., Briers, J.D., 1981. Flow visualization by means of single-exposure speckle photography. Opt. Commun. 37 (5), 326–330. Forsyth, D.A., Ponce, J., 2009. Computer Vision: a Modern Approach. Indian ed. Prentice Hall India. Fujii, H., Konishi, N., Yamamoto, Y., Ikawa, H., Ohura, T., 1987. Evaluation of blood flow by laser speckle image sensing; part 1. Appl. Opt. 26 (24). Fujii, H., Konishi, N., Lee, M.C., 2007. Blood flow analyses with laser speckle flowgraphy. Chin. Opt. Lett. 5, S235–S236. Gefen, A., 2012. Bioengineering Research of Chronic Wounds: a Multidisciplinary Study Approach, Studies in Mechanobiology, Tissue Engineering and Biomaterials. Springer London Ltd. Gonzalez, R.C., 2014. Woods RE. Digital Image Processing, Thrid ed. Pearson. Gupta, N., et al., 2004. An Indian community-based epidemiological study of wounds. J. Wound Care 13 (8), 323–325. Hay, R., Bendeck, S.E., Chen, S., et al., 2006. Skin diseases. In: Jamison, D.T., Breman, J.G., Measham, A.R., et al. (Eds.), Disease Control Priorities in Developing Countries, second ed. World Bank, Washington (DC) (Chapter 37). Joseph, N., Kumar, G.S., Nelliyanil, M., 2014. Skin diseases and conditions among students of a medical college in Southern India. Indian Dermatol. 5, 19–24. Karthikeyan, K., Thappa, D.M., Jeevankumar, B., 2004. Pattern of pediatric dermatoses in a referral Center in South India. Indian Pediatr. 41, 373–377. Kruijt, B., de Bruijn, H.S., van der Ploeg-van den Heuvel, A., Sterenborg, H.J.C.M., Robinson, D.J., 2006. Laser speckle imaging of dynamic changes in flow during photodynamic therapy. Lasers Med. Sci. 21, 208–212. Miao, P., Rege, A., Li, N., Thakor, N.V., Tong, S., 2010. High resolution cerebral blood flow imaging by registered laser speckle contrast analysis. IEEE Trans. Biomed. Eng. 57 (5), 1152–1157.

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