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Quality test protocol to dynamic laser speckle analysis
Optics and Lasers in Engineering 61 (2014) 8–13
Contents lists available at ScienceDirect
Optics and Lasers in Engineering journal homepage: www.elsevier.com/locate/optlaseng
Quality test protocol to dynamic laser speckle analysis Junio Moreira a, R.R. Cardoso a, R.A. Braga b,n a b
Brazilian Bioethanol Science and Technology Laboratory, Lavras, MG, Brazil Department of Engineering, Federal University of Lavras UFLA, CP 3037 Lavras, MG, Brazil
art ic l e i nf o
a b s t r a c t
Article history: Received 14 January 2014 Received in revised form 14 April 2014 Accepted 17 April 2014 Available online 14 May 2014
Biospeckle (BSL) is a phenomenon that develops when a dynamic process occurs under laser illumination in a material containing considerable information on biological and non-biological activity. Analysis of such data has been a challenge because of complex interaction between light and material, and therefore, of high sensitivity of the BSL and variability of biological material. This study was aimed at improving the robustness of BSL techniques and thus reducing its subjectivity and dependence on human expertise. The work presents a protocol that aids in a quality test of three key features of the speckle patterns, particularly those related to saturation, under-exposure, homogeneity and contrast of the grains. The quality test was carried out with data from a maize seed and a paint drying under biospeckle laser monitoring, where it was possible to identify some problems in the images related to the tested features. The results establish the feasibility of the quality test control, which form the early steps of the biospeckle laser analysis, and serves in helping the judgment of the setup. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Biospeckle Quality test Normalization Standard deviation
1. Introduction For analyzing the signals from dynamic laser speckle patterns digital image processing and statistical approaches are used. The analysis can be divided into two parts: graphical analysis and numerical analysis. Graphical analysis results in maps showing spatial variability of BSL activity level, for instance the Fujii Method [1], the Generalised Differences (GD) [2], the Laser Analysis of Speckle Contrast (LASCA) [3], and the temporal difference method [4]. Numerical analysis results in numbers related to the activity of boiling by means of such techniques as the Inertia Moment (IM) [5], the autocorrelation [6], the entropy [7] or even the contrast [3]. However, the complexity of the phenomenon, arising from the multitude of variables involved in dynamic speckle formation, particularly in biological materials, adds to the sensitiveness of the technique, and hence demands a continuous development of the traditional techniques, besides standardization of the experimental configurations and thus of the outputs. Many proposals are available in literature on improving the traditional techniques for BSL analysis [8], for example, reducing the time consuming of the GD procedure [9], or changing the IM method by replacing the
n
Corresponding author. E-mail address: [email protected]fla.br (R.A. Braga).
http://dx.doi.org/10.1016/j.optlaseng.2014.04.005 0143-8166/& 2014 Elsevier Ltd. All rights reserved.
squared differences with the absolute values of the differences (AVD) [10-11]. It is also possible to see works dealing with answers in frequency domain [12–14]. Autocorrelation of speckle patterns is claimed to be the best approach to monitor the biological activity linked to temperature evolution [15]. Despite numerous efforts to improve the traditional methods and their outputs, there is still no standardization, particularly in the image acquisition steps. The main hypothesis of this work is based on the belief that there is still scope for improvement and standardization of BSL results, particularly to avoid subjectivity in image acquisition. Therefore, this work was aimed at developing a protocol to address the requirements for ideal assembling of data before the main analysis, called here ‘Quality Test Protocol’.
2. Theory 2.1. Saturation Saturation of image is a feature that informs whether the pixels in the image have their values equal to zero (0) or 255, the former being the lower level of the grey scale (based on 8 bit) and the latter of the upper level. Therefore, if there is a portion of the image with values equal to 0 or 255, one can say that that portion of the image is an underexposure or saturation,
J. Moreira et al. / Optics and Lasers in Engineering 61 (2014) 8–13
9
respectively, which will certainly compromise the BSL analysis [16]. The saturation test aims to classify the areas and present them graphically to facilitate the best judgment of the user. 2.2. Contrast Contrast is a measure of activity [3] and is applied to single images of the speckle for analyzing the integration period of the camera. It is adopted as a useful tool to measure speckle activity online [17]; it is also useful to test speckle patterns in offline measurements. Therefore, the idea of the test is to evaluate the contrast of the grains in a single image of the collection and find out if the activity of the material is already expressed. Thus, even if one single speckle pattern presents low contrast, the velocity of the phenomenon under monitoring is certainly higher than what is expected and the analysis of the image collection will lead to erroneous conclusions. Eq. (1) shows the contrast test. C¼
sx;y
ð1Þ
〈I〉
where 〈I〉 and s represent spatial average and standard deviation of gray level intensity, respectively, of each observation window.
Fig. 1. Illustration of image divided into small windows for homogeneity calculation.
3. Methodology All the examples (data) used in the tests are well known in the literature with, at least, one feature (contrast, homogeneity or saturation) compromised. The Quality Test Protocol implemented simultaneously all the three tests proposed and compared them by biasing the adjustment of the size of the windows in the Regions of Interest (ROI) and the threshold of saturation, contrast and homogeneity. The data from wet maize seeds had 64 participant images and that from the paint drying 128 participant images. 3.1. Saturation
2.3. Homogeneity The third test in the protocol helps in providing the association of graphical and numerical results [21], when one desires to get a numerical analysis from a heterogeneous sample. Therefore, the test evaluates the level of homogeneity in the whole image and identifies the areas where the activity measured by numerical methods, such as the Inertia Moment [5] or the AVD [10], can be considered to be far from the borders of areas with different activity. IM ¼ ∑i;j
OCMij ji jj normalization
ð2Þ
where OCM is the occurrence matrix of successive values in the Time History Speckle Pattern [5], and i and j variables are the dimensions of the OCM matrix. The normalization provides the summation of the values of the occurrence, in each line, of the OCM, which equals 1. The analysis using the IM or AVD requires that some intermediate steps be taken before adopting Eq. (2). Therefore, the first step is to construct the THSP [5], which is a matrix formed by lines extracted from the participant images. The time history of those lines is therefore the matrix THSP, which forms the base for constructing the OCM matrix [5], where the sequence of pixels in the THSP matrix is registered in the squared OCM matrix as having the dimensions of 1 to 256 pixel. It is performed on a collection of images divided into small windows with M N pixels elected by the user. The next step is to measure the spatial variability of the activity by observing the value of the Moment of Inertia (IM) or other numeric quantifier in each window of study defined by the similarity between each IM and its neighborhood [21]. In Fig. 1, the homogeneity level for window 5 (IM5) is given by Eq. (3): Homogeneity ¼ 100
sðIM2 ; IM4 ; IM5 ; IM6 ; IM8 Þ μðIM2 ; IM4 ; IM5 ; IM6 ; IM8 Þ
ð3Þ
where the variables s and m mean, respectively, the standard deviation and the mean value of the collection of images. The number of participant images to be adopted can be set by the user.
The test for evaluation of saturation or sub-exposition was conducted over all the images of the collection, which were divided into small windows with M N pixels. The variables M and N represent respectively the row and the column of each observation window within the whole matrix. This work presents the results related to only the windows of 30 30 pixel (M N), although many other sizes were tested. The test has the flexibility of allowing the user to choose the best window, besides providing the feedback needed for adjusting the regions of interest (ROI). The values of M and N were set to 30 after some tests regarding the size of the image under analysis, particularly that of the maize, which had a size of 256 490 pixel, resulting in 8 16 windows and covering in detail all the ROI’s in the embryo, in the endosperm and in the neighborhood of the crack. The grey level of each pixel was evaluated throughout the collection of images, and based on the grey levels, the areas were classified into three kinds: saturation, normal or sub-exposition. The data used in the grey level test was that of the classic collection of maize seed images [18] with a clear area of saturation within its embryo. 3.2. Contrast The second parameter, the contrast level, was acquired for each window of M N pixels to collect information about grain formation in speckle pattern [3]. The contrast test was conducted on a well-known example of paint drying [9,19,20], where the first stages after applying the paint were compared to the subsequent stages. The test was also done with a heterogeneous image, such as that of the maize seed, to illustrate the outcome of the proposed procedure. The windows in the example were of 30 30 pixel. 3.3. Homogeneity Homogeneity, the last test proposed for the quality protocol, was evaluated by means of the Inertia Moment (IM) in each observation window, with a window size of 30 30 pixel. In this
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J. Moreira et al. / Optics and Lasers in Engineering 61 (2014) 8–13
case, it is considered that reduction in spatial information does not affect IM activity values significantly [22].
4. Results and discussion 4.1. Saturation In the processed maize seed image, the saturation in the embryo's core caused the formation of a dark area (Fig. 2a), which leads to erroneous information, because the dark area should be clearly linked to the high activity expected. Saturated or sub-exposed areas in BSL images of the material under study can create false activity impressions or mask real activity. While knowledge about the existence of these extremes in the images is important, locating saturated or sub-exposed areas is even more important in many experiments, such as in seeds viability tests. The activity map of a maize seed (see Fig. 2a), generated by GD method, shows lack of activity in the middle region of the embryo, which is addressed by the saturated area (see Fig. 2b), using a
participant image of the collection. The area relating to histogram shown in Fig. 2c can be considered normal and that shown in Fig. 2d saturated. The histograms inform how many times the gray levels occurred within the delimited area, and in what region of the gray levels does the image window fall. For example, in Fig. 2d, the level of occurrence in the histogram is as high as 255. It could certainly be even higher, but was limited to 255, implying that it was saturated. The term participant image is used here to refer to an image of the collection of frames, before they are processed. The whole image can be classified, as proposed, by the windows (see Fig. 3). A saturation map can be plotted, based on the number of occurrences of value 255 in the center of the embryo area, to evaluate the quality of the image before taking up the main analysis. This allows for a correction of the set up in accordance with the extension of the saturation, or even subexposition, as also in accordance with the region being analyzed. In this test, the maximum level of gray with a value of 255 is defined as saturated. The samples were used to image the windows of 30 30 pixel size. The amounts present in each window comprising 900 pixel represent the percentage of saturated pixels. In the red windows, it can be seen that this value is
Fig. 2. Result of (a) GD in a maize seed; (b) a frame excerpted from the whole collection; (c) with the histogram of an area without saturation; (d) with the histogram of an area with saturation.
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Fig. 3. Percentage occurrence of saturation in maize seed over 50% of the pixels in the windows of 30 30 shown in gray (a) presented by values and (b) presented by color representation.
higher than 50%, implying that they form a region of greater saturation. The definition of the threshold of saturation will vary in accordance with the application as well as with the place where the analysis is to be performed, i.e. one can have a saturated area out of the ROI, and the analysis can be carried out without compromising the quality of results. In Fig. 3, the threshold of saturation was the occurrence (in percentage) of pixels with values equal to 255 (in gray level), higher than 50% over the pixels of the whole window. The subexposition was not relevant because its main occurrence was only out of the seed and therefore not of interest for present study. The size of the window was 30 30; however, other sizes were tested if they would fit into the desired regions of interest (ROI). The user can as well adopt a running window to increase the resolution, but as the quality test aims to find the best ROI to perform further analysis, i.e. the numerical approaches, the fixed windows is recommended. This comment about running windows holds good for contrast as well as homogeneity. 4.2. Contrast The contrast approach to the images, the second test of quality, was tested using Eq. (1), and an example of its influence on the results is shown in Fig. 4, where the paint drying phenomenon was monitored since its beginning. It is known that, in the case of paint drying, immediately after painting, because of high volatilization, and therefore of typical under-sampling, a chaotic behavior occurs. Fig. 4a presents a numerical analysis, using IM method [14], to quantify the activity of ink evaporation over time, and
Fig. 4b shows an example of participant images at 5, 15, 25, 35, 45 and 55 min after painting an image group. It can be observed that, in the first moment, the numerical result was inconsistent with the expectation. Volatilization activity is expected to be more frenetic a few moments just after painting, but the numerical output shown by IM was not in accordance to the activity expected. This is because the rate of evaporation is greater than the rate of image acquisition, and that results in under-sampling, an aliasing of the signal. The speckle grains looked blurred, which is evident in the first participant images of Fig. 4. To confirm this point, the contrast was performed, according to Eq. (2), in the six stages addressed. Fig. 5 presents the mean contrast for each period of observation. The contrast value is lower in the first imagery group, resulting in a blurred speckle pattern, and consequently hindering the numerical analysis performed by IM method. The reduction in contrast during the first stage can explain the reduction in IM values at that moment, and confirm the time of undersampling in that first observation. Thus, that was a key factor to eliminate that stage from the analysis or to alert the user of undersampling. In Fig. 6, it can be observed that in regions, where greater activity quality test was performed with contrast, the speckle grains were not well defined, indicating that the contrast was low. Thus, the clear area in the embryo shows that, even with one participant image, it is possible to create the map of activity, which in effect means that the analysis of the BSL can be compromised when the whole collection of images is used to analyze the data, such as the misleading observed in the paint drying.
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J. Moreira et al. / Optics and Lasers in Engineering 61 (2014) 8–13
Fig. 4. Results of paint drying process measured by (a) IM values dimensionless and (b) participant images in some moments.
Fig. 5. Mean value of the contrast for each image group.
In areas of low activity, the speckle grains are tightly defined indicating high contrast. 4.3. Homogeneity The homogeneity test in a seed can be observed in Fig. 7, where the colours define the level of homogeneity in the windows. The higher the homogeneity, the closer is the color to blue, and the lower the homogeneity, the closer is the color to red. In Fig. 7, the embryo area shows windows with lower homogeneity than the areas in the endosperm, which are painted in blue. The transition areas present low homogeneity. The areas within the embryo or the endosperm, which present blue or close to blue colors, can be considered good areas to carry out numerical analysis. However, a single test, such as the homogeneity test, can lead the user to wrong conclusions. For instance, in Fig. 7, the area of high homogeneity in the embryo is considered bad by the contrast test for analysis. Therefore, the proposed tests, when applied together, proved to be effective in eliminating subjectivism in illumination experiments using mathematical tools and thus the setup can be considered validated for acquiring images of reliable quality. These
Fig. 6. Results of the method of contrast in maize seed, measured in the windows of 30 30 in gray.
techniques of the Quality Test Protocol help in standardizing the first step of the process which increases the robustness and thereby the accessibility of the technique, and in creating some standards to use this technique.
5. Conclusions The Quality Test Protocol provides a way to reduce subjectivity in the experimental configuration setup and to avoid undesirable results obtained from using the saturation, the contrast and the homogeneity tests. Saturation can be evaluated, by means of
J. Moreira et al. / Optics and Lasers in Engineering 61 (2014) 8–13
Fig. 7. Quality Test homogeneity applied to the collection of maize seed images with red (light gray) areas representing low homogeneity and blue (dark gray)areas high homogeneity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the intensity of the pixels in each window of the image, contrast by means of the contrast of the speckle grains in participant images, and homogeneity by means of the level of activity in the neighborhood of each window within the image. The maps generated would offer a guide to the user, and address the possible problems in experimental configuration.
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[9] Saúde, AV, Menezes, FS, Freitas, PLS, Rabelo, GF, Braga, RA, On generalized differences for Biospeckle image analysis. In: Proceedings of the 23rd SIBGRAPI conference on graphics, patterns and images; (2010) 209–215. [10] Braga RA, Nobre CMB, Costa AG, Sáfadi T, Costa FM. Evaluation of activity through dynamic laser speckle using the absolute value of the differences. Opt Commun 2011:646–50. [11] Ansari MZ, Nirala AK. Assessment of bio-activity using the methods of inertia moment and absolute value of the differences Optik—International. J Light Electron Opt 2012;124:2180–6. [12] Sendra GH, Arizaga R, Rabal HJ, Trivi M. Decomposition of biospeckle images in temporary spectral bands. Opt Lett 2005;30:1641–3. [13] Nobre CMB, Braga RA, Costa AG, Cardoso RR, da Silva WS, Sáfadi T. Biospeckle laser spectral analysis under Inertia Moment, Entropy and Cross-Spectrum methods. Opt Commun 2009;282:2236–42. [14] Braga RA, Horgan GW, Enes AM, Miron D, Rabelo GF, Filho JBB. Biological feature isolation by wavelets in biospeckle laser images. Comput Electron Agr 2007;58:123–32. [15] Zdunek A, Muravsky LI, Frankevych L, Konstankiewicz K. New nondestructive method based on spatial-temporal speckle correlation technique for evaluation of apples quality during shelf-life. Int Agrophys 2007;21:305–10. [16] Arizaga, R Methods of dynamic speckle analysis: statistical analysis. In: Dynamic laser speckle and applications editor Rabal HJ and Braga RA (2008) 95–114. [17] Briers JD, Webster S. Laser speckle contrast analysis (LASCA): a nonscanning, full field technique for monitoring capillary blood flow. J Biomed Opt 1996;1:174–9. [18] Braga RA, Vieira MGGC, Pinho EVRV, Rabal HJ, Dal Fabbro IM, Souza A. Potencial do bio-speckle laser para avaliação da viabilidade de sementes. Cienc. Agrotecnol. 2001;25(3):645–9. [19] Blotta E, Ballarín V, Brun M, Rabal HJ. Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings. Signal Process 2011;91:2395–403. [20] Amalvy J, Lasquibar C, Arizaga R, Rabal HJ, Trivi M. Application of dynamic speckle interferometry to the drying of coatings. Prog Org Coat 2001 89–99. [21] Braga RA, Cardoso RR, Bezerra PS, Wouters F, Sampaio GR, Varaschin MS. Biospeckle numerical values over spectral image maps of activity. Opt Commun 2012;285:553–61. [22] Braga RA, Silva B, Rabelo GF, Marques R, Enes AM, Cap N, et al. Reliability of biospeckle image analysis. Opt Lasers Eng 2007;45:390–5.
Junio Moreira had his BA degree in Systems of Information from the Pontifical Catholic University of Minas Gerais – Arcos – Brazil (2006) and MSc degree in Systems Engineering from UFLA – Brazil 2012, with specialization in biospeckle image processing. He has been working with Technology of Information for over ten years.
Acknowledgements This work was partially supported by Federal University of Lavras, CNPq 302805/2012-5, Fapemig CAG PPM213-13, Capes, and Finep from Brazil. Special thanks are due to Hector Jorge Rabal.
References [1] Fujii H. Blood-flow observed by time-varying laser speckle. Opt Lett 1985;10:104–6. [2] Arizaga R. Display of local activity using dynamical speckle patterns. Opt Eng 2002;41:287–94. [3] Briers JD. Wavelength dependence of intensity fluctuations in laser speckle patterns from biological specimens. Opt Commun 1975;13:324. [4] Martí-López L, Cabrera H, Martínez-Celorio RA, González-Peña R. Temporal difference method for processing dynamic speckle patterns. Opt Commun 2010;283:4972–7. [5] Arizaga R, Trivi M. Speckle time evolution characterization by the cooccurrence matrix analysis. Opt Laser Technol 1999;31:163–9. [6] Xu Z, Joenathan C, Khorana BM. Temporal and spatial properties of the timevarying speckles of botanical specimens. Opt Eng 1995;34:1487–502. [7] Passoni I I, Dai Pra A, Rabal HJ, Trivi M, Arizaga R. Dynamic speckle processing using wavelets based entropy. Opt Commun 2005;246:219–28. [8] Zdunek A, Adamiak A, Pieczywek PM, Kurenda A. The biospeckle method for the investigation of agricultural crops: a review. Opt. Lasers Eng. 2013 276–85.
Rafael Rodrigues Cardoso graduated in agricultural engineering from Federal University of Lavras. He completed his masters in Agricultural Engineering from the same university with specialization in agricultural machinery and automation. He currently works at a Brazilian national laboratory, Brazilian Bioethanol Science and Technology Laboratory (CTBE) of National Center of Energy and Materials Research, where he also acts as a control and automation project engineer for new technologies applied for sugarcane mechanized agriculture. His research fields include sensing, imaging and signal processing for agriculture purposes.
Roberto Alves Braga Jr, had his masters degree in Electrical Engineering from Minas Gerais Federal University, Brazil, and Doctoral degree in Agricultural Engineering from State University of Campinas, Brazil. He has been an Associate Professor at Federal University of Lavras since 1996, where he teaches electricity and automation, besides carrying out research on optical instrumentation. He has been on Sabbatical leave at BIOSS Scotland in 2005 and 2008 and occupied Senior stage at BIOSS and JHI, Scotland in 2011. He has more than 40 papers to his credit, the majority of which were published in international journals. Besides, he authored chapters of books and obtained some patents. Also, he co-edited the book entitled “Dynamic Laser Speckle and Applications by CRC” in 2008. He served as the coordinator of the undergraduate BA in Control and Automation Engineering. He coordinates the post-graduate course in Automation and Systems Engineering. He has a grant level of 1D related to Research Productivity by CNPq.
Contents lists available at ScienceDirect
Optics and Lasers in Engineering journal homepage: www.elsevier.com/locate/optlaseng
Quality test protocol to dynamic laser speckle analysis Junio Moreira a, R.R. Cardoso a, R.A. Braga b,n a b
Brazilian Bioethanol Science and Technology Laboratory, Lavras, MG, Brazil Department of Engineering, Federal University of Lavras UFLA, CP 3037 Lavras, MG, Brazil
art ic l e i nf o
a b s t r a c t
Article history: Received 14 January 2014 Received in revised form 14 April 2014 Accepted 17 April 2014 Available online 14 May 2014
Biospeckle (BSL) is a phenomenon that develops when a dynamic process occurs under laser illumination in a material containing considerable information on biological and non-biological activity. Analysis of such data has been a challenge because of complex interaction between light and material, and therefore, of high sensitivity of the BSL and variability of biological material. This study was aimed at improving the robustness of BSL techniques and thus reducing its subjectivity and dependence on human expertise. The work presents a protocol that aids in a quality test of three key features of the speckle patterns, particularly those related to saturation, under-exposure, homogeneity and contrast of the grains. The quality test was carried out with data from a maize seed and a paint drying under biospeckle laser monitoring, where it was possible to identify some problems in the images related to the tested features. The results establish the feasibility of the quality test control, which form the early steps of the biospeckle laser analysis, and serves in helping the judgment of the setup. & 2014 Elsevier Ltd. All rights reserved.
Keywords: Biospeckle Quality test Normalization Standard deviation
1. Introduction For analyzing the signals from dynamic laser speckle patterns digital image processing and statistical approaches are used. The analysis can be divided into two parts: graphical analysis and numerical analysis. Graphical analysis results in maps showing spatial variability of BSL activity level, for instance the Fujii Method [1], the Generalised Differences (GD) [2], the Laser Analysis of Speckle Contrast (LASCA) [3], and the temporal difference method [4]. Numerical analysis results in numbers related to the activity of boiling by means of such techniques as the Inertia Moment (IM) [5], the autocorrelation [6], the entropy [7] or even the contrast [3]. However, the complexity of the phenomenon, arising from the multitude of variables involved in dynamic speckle formation, particularly in biological materials, adds to the sensitiveness of the technique, and hence demands a continuous development of the traditional techniques, besides standardization of the experimental configurations and thus of the outputs. Many proposals are available in literature on improving the traditional techniques for BSL analysis [8], for example, reducing the time consuming of the GD procedure [9], or changing the IM method by replacing the
n
Corresponding author. E-mail address: [email protected]fla.br (R.A. Braga).
http://dx.doi.org/10.1016/j.optlaseng.2014.04.005 0143-8166/& 2014 Elsevier Ltd. All rights reserved.
squared differences with the absolute values of the differences (AVD) [10-11]. It is also possible to see works dealing with answers in frequency domain [12–14]. Autocorrelation of speckle patterns is claimed to be the best approach to monitor the biological activity linked to temperature evolution [15]. Despite numerous efforts to improve the traditional methods and their outputs, there is still no standardization, particularly in the image acquisition steps. The main hypothesis of this work is based on the belief that there is still scope for improvement and standardization of BSL results, particularly to avoid subjectivity in image acquisition. Therefore, this work was aimed at developing a protocol to address the requirements for ideal assembling of data before the main analysis, called here ‘Quality Test Protocol’.
2. Theory 2.1. Saturation Saturation of image is a feature that informs whether the pixels in the image have their values equal to zero (0) or 255, the former being the lower level of the grey scale (based on 8 bit) and the latter of the upper level. Therefore, if there is a portion of the image with values equal to 0 or 255, one can say that that portion of the image is an underexposure or saturation,
J. Moreira et al. / Optics and Lasers in Engineering 61 (2014) 8–13
9
respectively, which will certainly compromise the BSL analysis [16]. The saturation test aims to classify the areas and present them graphically to facilitate the best judgment of the user. 2.2. Contrast Contrast is a measure of activity [3] and is applied to single images of the speckle for analyzing the integration period of the camera. It is adopted as a useful tool to measure speckle activity online [17]; it is also useful to test speckle patterns in offline measurements. Therefore, the idea of the test is to evaluate the contrast of the grains in a single image of the collection and find out if the activity of the material is already expressed. Thus, even if one single speckle pattern presents low contrast, the velocity of the phenomenon under monitoring is certainly higher than what is expected and the analysis of the image collection will lead to erroneous conclusions. Eq. (1) shows the contrast test. C¼
sx;y
ð1Þ
〈I〉
where 〈I〉 and s represent spatial average and standard deviation of gray level intensity, respectively, of each observation window.
Fig. 1. Illustration of image divided into small windows for homogeneity calculation.
3. Methodology All the examples (data) used in the tests are well known in the literature with, at least, one feature (contrast, homogeneity or saturation) compromised. The Quality Test Protocol implemented simultaneously all the three tests proposed and compared them by biasing the adjustment of the size of the windows in the Regions of Interest (ROI) and the threshold of saturation, contrast and homogeneity. The data from wet maize seeds had 64 participant images and that from the paint drying 128 participant images. 3.1. Saturation
2.3. Homogeneity The third test in the protocol helps in providing the association of graphical and numerical results [21], when one desires to get a numerical analysis from a heterogeneous sample. Therefore, the test evaluates the level of homogeneity in the whole image and identifies the areas where the activity measured by numerical methods, such as the Inertia Moment [5] or the AVD [10], can be considered to be far from the borders of areas with different activity. IM ¼ ∑i;j
OCMij ji jj normalization
ð2Þ
where OCM is the occurrence matrix of successive values in the Time History Speckle Pattern [5], and i and j variables are the dimensions of the OCM matrix. The normalization provides the summation of the values of the occurrence, in each line, of the OCM, which equals 1. The analysis using the IM or AVD requires that some intermediate steps be taken before adopting Eq. (2). Therefore, the first step is to construct the THSP [5], which is a matrix formed by lines extracted from the participant images. The time history of those lines is therefore the matrix THSP, which forms the base for constructing the OCM matrix [5], where the sequence of pixels in the THSP matrix is registered in the squared OCM matrix as having the dimensions of 1 to 256 pixel. It is performed on a collection of images divided into small windows with M N pixels elected by the user. The next step is to measure the spatial variability of the activity by observing the value of the Moment of Inertia (IM) or other numeric quantifier in each window of study defined by the similarity between each IM and its neighborhood [21]. In Fig. 1, the homogeneity level for window 5 (IM5) is given by Eq. (3): Homogeneity ¼ 100
sðIM2 ; IM4 ; IM5 ; IM6 ; IM8 Þ μðIM2 ; IM4 ; IM5 ; IM6 ; IM8 Þ
ð3Þ
where the variables s and m mean, respectively, the standard deviation and the mean value of the collection of images. The number of participant images to be adopted can be set by the user.
The test for evaluation of saturation or sub-exposition was conducted over all the images of the collection, which were divided into small windows with M N pixels. The variables M and N represent respectively the row and the column of each observation window within the whole matrix. This work presents the results related to only the windows of 30 30 pixel (M N), although many other sizes were tested. The test has the flexibility of allowing the user to choose the best window, besides providing the feedback needed for adjusting the regions of interest (ROI). The values of M and N were set to 30 after some tests regarding the size of the image under analysis, particularly that of the maize, which had a size of 256 490 pixel, resulting in 8 16 windows and covering in detail all the ROI’s in the embryo, in the endosperm and in the neighborhood of the crack. The grey level of each pixel was evaluated throughout the collection of images, and based on the grey levels, the areas were classified into three kinds: saturation, normal or sub-exposition. The data used in the grey level test was that of the classic collection of maize seed images [18] with a clear area of saturation within its embryo. 3.2. Contrast The second parameter, the contrast level, was acquired for each window of M N pixels to collect information about grain formation in speckle pattern [3]. The contrast test was conducted on a well-known example of paint drying [9,19,20], where the first stages after applying the paint were compared to the subsequent stages. The test was also done with a heterogeneous image, such as that of the maize seed, to illustrate the outcome of the proposed procedure. The windows in the example were of 30 30 pixel. 3.3. Homogeneity Homogeneity, the last test proposed for the quality protocol, was evaluated by means of the Inertia Moment (IM) in each observation window, with a window size of 30 30 pixel. In this
10
J. Moreira et al. / Optics and Lasers in Engineering 61 (2014) 8–13
case, it is considered that reduction in spatial information does not affect IM activity values significantly [22].
4. Results and discussion 4.1. Saturation In the processed maize seed image, the saturation in the embryo's core caused the formation of a dark area (Fig. 2a), which leads to erroneous information, because the dark area should be clearly linked to the high activity expected. Saturated or sub-exposed areas in BSL images of the material under study can create false activity impressions or mask real activity. While knowledge about the existence of these extremes in the images is important, locating saturated or sub-exposed areas is even more important in many experiments, such as in seeds viability tests. The activity map of a maize seed (see Fig. 2a), generated by GD method, shows lack of activity in the middle region of the embryo, which is addressed by the saturated area (see Fig. 2b), using a
participant image of the collection. The area relating to histogram shown in Fig. 2c can be considered normal and that shown in Fig. 2d saturated. The histograms inform how many times the gray levels occurred within the delimited area, and in what region of the gray levels does the image window fall. For example, in Fig. 2d, the level of occurrence in the histogram is as high as 255. It could certainly be even higher, but was limited to 255, implying that it was saturated. The term participant image is used here to refer to an image of the collection of frames, before they are processed. The whole image can be classified, as proposed, by the windows (see Fig. 3). A saturation map can be plotted, based on the number of occurrences of value 255 in the center of the embryo area, to evaluate the quality of the image before taking up the main analysis. This allows for a correction of the set up in accordance with the extension of the saturation, or even subexposition, as also in accordance with the region being analyzed. In this test, the maximum level of gray with a value of 255 is defined as saturated. The samples were used to image the windows of 30 30 pixel size. The amounts present in each window comprising 900 pixel represent the percentage of saturated pixels. In the red windows, it can be seen that this value is
Fig. 2. Result of (a) GD in a maize seed; (b) a frame excerpted from the whole collection; (c) with the histogram of an area without saturation; (d) with the histogram of an area with saturation.
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Fig. 3. Percentage occurrence of saturation in maize seed over 50% of the pixels in the windows of 30 30 shown in gray (a) presented by values and (b) presented by color representation.
higher than 50%, implying that they form a region of greater saturation. The definition of the threshold of saturation will vary in accordance with the application as well as with the place where the analysis is to be performed, i.e. one can have a saturated area out of the ROI, and the analysis can be carried out without compromising the quality of results. In Fig. 3, the threshold of saturation was the occurrence (in percentage) of pixels with values equal to 255 (in gray level), higher than 50% over the pixels of the whole window. The subexposition was not relevant because its main occurrence was only out of the seed and therefore not of interest for present study. The size of the window was 30 30; however, other sizes were tested if they would fit into the desired regions of interest (ROI). The user can as well adopt a running window to increase the resolution, but as the quality test aims to find the best ROI to perform further analysis, i.e. the numerical approaches, the fixed windows is recommended. This comment about running windows holds good for contrast as well as homogeneity. 4.2. Contrast The contrast approach to the images, the second test of quality, was tested using Eq. (1), and an example of its influence on the results is shown in Fig. 4, where the paint drying phenomenon was monitored since its beginning. It is known that, in the case of paint drying, immediately after painting, because of high volatilization, and therefore of typical under-sampling, a chaotic behavior occurs. Fig. 4a presents a numerical analysis, using IM method [14], to quantify the activity of ink evaporation over time, and
Fig. 4b shows an example of participant images at 5, 15, 25, 35, 45 and 55 min after painting an image group. It can be observed that, in the first moment, the numerical result was inconsistent with the expectation. Volatilization activity is expected to be more frenetic a few moments just after painting, but the numerical output shown by IM was not in accordance to the activity expected. This is because the rate of evaporation is greater than the rate of image acquisition, and that results in under-sampling, an aliasing of the signal. The speckle grains looked blurred, which is evident in the first participant images of Fig. 4. To confirm this point, the contrast was performed, according to Eq. (2), in the six stages addressed. Fig. 5 presents the mean contrast for each period of observation. The contrast value is lower in the first imagery group, resulting in a blurred speckle pattern, and consequently hindering the numerical analysis performed by IM method. The reduction in contrast during the first stage can explain the reduction in IM values at that moment, and confirm the time of undersampling in that first observation. Thus, that was a key factor to eliminate that stage from the analysis or to alert the user of undersampling. In Fig. 6, it can be observed that in regions, where greater activity quality test was performed with contrast, the speckle grains were not well defined, indicating that the contrast was low. Thus, the clear area in the embryo shows that, even with one participant image, it is possible to create the map of activity, which in effect means that the analysis of the BSL can be compromised when the whole collection of images is used to analyze the data, such as the misleading observed in the paint drying.
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Fig. 4. Results of paint drying process measured by (a) IM values dimensionless and (b) participant images in some moments.
Fig. 5. Mean value of the contrast for each image group.
In areas of low activity, the speckle grains are tightly defined indicating high contrast. 4.3. Homogeneity The homogeneity test in a seed can be observed in Fig. 7, where the colours define the level of homogeneity in the windows. The higher the homogeneity, the closer is the color to blue, and the lower the homogeneity, the closer is the color to red. In Fig. 7, the embryo area shows windows with lower homogeneity than the areas in the endosperm, which are painted in blue. The transition areas present low homogeneity. The areas within the embryo or the endosperm, which present blue or close to blue colors, can be considered good areas to carry out numerical analysis. However, a single test, such as the homogeneity test, can lead the user to wrong conclusions. For instance, in Fig. 7, the area of high homogeneity in the embryo is considered bad by the contrast test for analysis. Therefore, the proposed tests, when applied together, proved to be effective in eliminating subjectivism in illumination experiments using mathematical tools and thus the setup can be considered validated for acquiring images of reliable quality. These
Fig. 6. Results of the method of contrast in maize seed, measured in the windows of 30 30 in gray.
techniques of the Quality Test Protocol help in standardizing the first step of the process which increases the robustness and thereby the accessibility of the technique, and in creating some standards to use this technique.
5. Conclusions The Quality Test Protocol provides a way to reduce subjectivity in the experimental configuration setup and to avoid undesirable results obtained from using the saturation, the contrast and the homogeneity tests. Saturation can be evaluated, by means of
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Fig. 7. Quality Test homogeneity applied to the collection of maize seed images with red (light gray) areas representing low homogeneity and blue (dark gray)areas high homogeneity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the intensity of the pixels in each window of the image, contrast by means of the contrast of the speckle grains in participant images, and homogeneity by means of the level of activity in the neighborhood of each window within the image. The maps generated would offer a guide to the user, and address the possible problems in experimental configuration.
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[9] Saúde, AV, Menezes, FS, Freitas, PLS, Rabelo, GF, Braga, RA, On generalized differences for Biospeckle image analysis. In: Proceedings of the 23rd SIBGRAPI conference on graphics, patterns and images; (2010) 209–215. [10] Braga RA, Nobre CMB, Costa AG, Sáfadi T, Costa FM. Evaluation of activity through dynamic laser speckle using the absolute value of the differences. Opt Commun 2011:646–50. [11] Ansari MZ, Nirala AK. Assessment of bio-activity using the methods of inertia moment and absolute value of the differences Optik—International. J Light Electron Opt 2012;124:2180–6. [12] Sendra GH, Arizaga R, Rabal HJ, Trivi M. Decomposition of biospeckle images in temporary spectral bands. Opt Lett 2005;30:1641–3. [13] Nobre CMB, Braga RA, Costa AG, Cardoso RR, da Silva WS, Sáfadi T. Biospeckle laser spectral analysis under Inertia Moment, Entropy and Cross-Spectrum methods. Opt Commun 2009;282:2236–42. [14] Braga RA, Horgan GW, Enes AM, Miron D, Rabelo GF, Filho JBB. Biological feature isolation by wavelets in biospeckle laser images. Comput Electron Agr 2007;58:123–32. [15] Zdunek A, Muravsky LI, Frankevych L, Konstankiewicz K. New nondestructive method based on spatial-temporal speckle correlation technique for evaluation of apples quality during shelf-life. Int Agrophys 2007;21:305–10. [16] Arizaga, R Methods of dynamic speckle analysis: statistical analysis. In: Dynamic laser speckle and applications editor Rabal HJ and Braga RA (2008) 95–114. [17] Briers JD, Webster S. Laser speckle contrast analysis (LASCA): a nonscanning, full field technique for monitoring capillary blood flow. J Biomed Opt 1996;1:174–9. [18] Braga RA, Vieira MGGC, Pinho EVRV, Rabal HJ, Dal Fabbro IM, Souza A. Potencial do bio-speckle laser para avaliação da viabilidade de sementes. Cienc. Agrotecnol. 2001;25(3):645–9. [19] Blotta E, Ballarín V, Brun M, Rabal HJ. Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings. Signal Process 2011;91:2395–403. [20] Amalvy J, Lasquibar C, Arizaga R, Rabal HJ, Trivi M. Application of dynamic speckle interferometry to the drying of coatings. Prog Org Coat 2001 89–99. [21] Braga RA, Cardoso RR, Bezerra PS, Wouters F, Sampaio GR, Varaschin MS. Biospeckle numerical values over spectral image maps of activity. Opt Commun 2012;285:553–61. [22] Braga RA, Silva B, Rabelo GF, Marques R, Enes AM, Cap N, et al. Reliability of biospeckle image analysis. Opt Lasers Eng 2007;45:390–5.
Junio Moreira had his BA degree in Systems of Information from the Pontifical Catholic University of Minas Gerais – Arcos – Brazil (2006) and MSc degree in Systems Engineering from UFLA – Brazil 2012, with specialization in biospeckle image processing. He has been working with Technology of Information for over ten years.
Acknowledgements This work was partially supported by Federal University of Lavras, CNPq 302805/2012-5, Fapemig CAG PPM213-13, Capes, and Finep from Brazil. Special thanks are due to Hector Jorge Rabal.
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Rafael Rodrigues Cardoso graduated in agricultural engineering from Federal University of Lavras. He completed his masters in Agricultural Engineering from the same university with specialization in agricultural machinery and automation. He currently works at a Brazilian national laboratory, Brazilian Bioethanol Science and Technology Laboratory (CTBE) of National Center of Energy and Materials Research, where he also acts as a control and automation project engineer for new technologies applied for sugarcane mechanized agriculture. His research fields include sensing, imaging and signal processing for agriculture purposes.
Roberto Alves Braga Jr, had his masters degree in Electrical Engineering from Minas Gerais Federal University, Brazil, and Doctoral degree in Agricultural Engineering from State University of Campinas, Brazil. He has been an Associate Professor at Federal University of Lavras since 1996, where he teaches electricity and automation, besides carrying out research on optical instrumentation. He has been on Sabbatical leave at BIOSS Scotland in 2005 and 2008 and occupied Senior stage at BIOSS and JHI, Scotland in 2011. He has more than 40 papers to his credit, the majority of which were published in international journals. Besides, he authored chapters of books and obtained some patents. Also, he co-edited the book entitled “Dynamic Laser Speckle and Applications by CRC” in 2008. He served as the coordinator of the undergraduate BA in Control and Automation Engineering. He coordinates the post-graduate course in Automation and Systems Engineering. He has a grant level of 1D related to Research Productivity by CNPq.