Research Article Research on Law s Mask Texture Analysis System Reliability

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1 Research Journal of Applie Sciences, Engineering an Technology 7(19): , 2014 DOI: /rjaset ISSN: ; e-issn: Maxwell Scientific Publication Corp. Submitte: November 04, 2013 Accepte: December 06, 2013 Publishe: May 15, 2014 Research Article Research on Law s Mask Texture Analysis System Reliability Gan Hong Seng, Tan Tian Swee an Hum Yan Chai Department of Biotechnology an Meical Engineering, Meical Device an Technology Group, Material an Manufacturing Rasearch Alliance (MMRA), Faculty of Bioscience an Meical Engineering, Universiti Technologi Malaysia, Skuai, Malaysia Abstract: Texture analysis of X-ray bone image using Laws mask for irect evaluation of the bone quality has been popular. Nevertheless, etaile reliability evaluation of the system classification has been relatively unknown. In this stuy, we will examine the reliability of the Laws mask system classification by using the confusion matrix approach. The precise etection system by using stanar eviation statistical escriptor is supporte by the true positive of 87.5% an true negative of 83.33%. In conclusion, the statistical analysis of the texture base osteoporosis etection system s reliability iscloses a true potential in this etection technique. Nevertheless, future researches shoul inclue a larger image atabase to enhance the reliability of the results. Keywors: Confusion matrix, osteoporosis, statistical escriptor, texture INTRODUCTION The occurrence of osteoporosis is unpreictable, characterize by its silent progression an ealy aftermaths. Osteoporosis is typically specifie by low bone ensity an micro-architectural eterioration an consequently leas to fragility in bone strength as well as susceptibility to fracture (Bauer an Link, 2009; Eastell, 2005; Hong Seng an Tian Swee, 2013a). The low bone ensity is efine by the excessive rate of bone resorption over the rate of bone formation. Among the prominent factor that contributes to the loss of bone strength is age. As people ages, the bone strength is reuce subsequently, cause by the bone mass loss. As a result, the cortical bone becomes thinner while the trabecular bone becomes looser. Bone Mineral Density (BMD) is the inirect measurement approach for osteoporosis, yet representing current gol stanar for osteoporosis evaluation. Generally, BMD measurement is base on the absolute value calle T score. T score is the number of stanar eviation at which the inicate values reflect the mass ensity conition of the bone. Accoring to Worl Health Organization (WHO), there are three classes of T score evaluations. The T score of normal population shoul lies above the value of -1. On the other han, osteopenia (prelue to osteoporosis) ranges between -1 an -2.5 while osteoporosis population woul have a T score below -2.5 (Bauer an Link, 2009). In meical image processing technique, Law s Mask (Laws, 1980) has receive wie acceptance in the meical image analysis. Kenneth Ivan Laws brought forwar the Laws masks iea in 1980, where the main contribution of his approach has been the filtering of images with specific masks create from the combination of one imensional Kernel vector in orer to assess the texture properties. The Laws masks match the pixel neighborhoo to the set of stanar masks to compute the texture properties of images. There are five types of masks, namely Level (L), Ege (E), Spot (S), Ripple (R) an Wave (W). In aition, this image processing technique coul be further ivie into 5 5 imensions an 3 3 imensions. The level vector emonstrates the average grey level or center weighte local average; ege vector resembles the graient operator, respons to the column an row steppe eges in an images; spot vector represents the spot extraction; ripples vector etects ripple from the image while wave vector respons to any image pixel changes. Thus, 25 possible combinations of ifferent masks are prouce, with each combination serves equal potential in unlocking the highly iscriminative information escribing the bone quality an conition. In this stuy, we propose to initiate a research on the reliability of the 5 5 imension Laws Mask texture analysis using the confusion matrix. The result of this experiment will contribute to the characterization accuracy of osteoporosis feature through appropriate classification criterions. Two statistical escriptors Corresponing Author: Tan Tian Swee, Department of Biotechnology an Meical Engineering, Meical Device an Technology Group, Material an Manufacturing Research Alliance (MMRA), Faculty of Bioscience an Meical Engineering, Universiti Teknologi Malaysia, Skuai, Malaysia This work is license uner a Creative Commons Attribution 4.0 International License (URL:

2 Res. J. App. Sci. Eng. Technol., 7(19): , 2014 namely mean an stanar eviation will reflect the accuracies of the system evelope base on the concept of Laws mask. Overview of the evaluation of system reliability: Through mean statistical escriptor an stanar eviation statistical escriptor, we have conucte the statistical evaluation to examine the reliability of the system for future osteoporosis etection using Laws masks technique. The mathematical formulas for the mean an stanar eviation statistical escriptor compute in this experiment have been illustrate in Eq. (1) an (2) (Hong Seng an Tian Swee, 2013b): Mean= SD= M N [ TRi, j] i= 0 j= 0 M N M N [ TRi, j Mean] i= 0 j= 0 M N 2 METHODOLOGY (1) (2) Development of osteoporosis texture analysis moel: Using six normal X-ray an one osteoporosis X-ray image, the image contrast is enhance using histogram equalization an resize. The esire features are extracte an parameterize through texture analysis. A winow size of 15 pixels is applie. We apply 5 5 imension Laws Mask, proucing a total of 25 masks. Table 1 illustrates the one imension convolution kernel of length 5 an Table 2 illustrates all 25 mask combinations generate in this experiment. Next, the image is texturally analyze accoring to selecte features. Finally, the ata is clustere by using the Eucliean istance base clustering. Table 1: The one imension convolution kernel of length 5 Kernel Matrices L 5 ( ) E 5 ( ) S 5 ( ) R 5 ( ) W 5 ( ) Table 2: The generation of 25 ifferent mask combination of 5 5 imension laws mask L 5L 5 L 5E 5 L 5S 5 L 5R 5 L 5W 5 E 5L 5 E 5E 5 E 5S 5 E 5R 5 E 5W 5 S 5L 5 S 5E 5 S 5S 5 S 5R 5 S 5W 5 W 5L 5 W 5E 5 W 5S 5 W 5R 5 W 5W 5 R 5L 5 R 5E 5 R 5S 5 R 5R 5 R 5W 5 The histogram with intensity level (0, L - 1) of the image is the ajuste histogram range. The normalize range coul be obtaine by normalizing the number of pixel with intensity r k, n k with the total number of pixel, N in the X-ray image, where the mathematical expression of the histogram normalization, p (n k ) is shown in Eq. (3). The normalize histogram represents an estimate of the likelihoo of intensity level n k to occur in the X-ray image (Gonzalez an Woos, 2007): nk p( rk ) = (3) N In this experiment, we view the input intensity variable, r in the X-ray image as ranom variables in the interval of (0, L - 1). The output of the intensity variable, s can be prouce through transformational function, T (r). The transformation is illustrate in Eq. (4). Next, we woul escript the ranom variable through Probability Density Function (PDF), in which the input intensity variable r an the transforme intensity s are contributing to the PDF of the transforme variable s shown in Eq. (5): Histogram equalization of X-ray images: Histogram equalization has been applie in numerous fiels, for example, computerize meical intervention (Gonzalez an Woos, 2007), meical image analysis (Yan Chai et al., 2013), image ocument processing, LCD isplay processing an raar. Because our experiment is not emphasizing on the contrast enhancement, we woul briefly explain the enhancement technique utilize in this stuy. The contrast between backgroun an bone is alreay very similar. Therefore, we have selecte to locally moify the histogram of X-ray image. Local histogram equalization is capable of preserving the local brightness feature by using small winow specifie for certain regions of interest. The winowing operation will filter the pixels an allows only blocks of pixels that fulfill the criterion of the specific winow (Wang an Zhongfu, 2005; Chauhan an Bhaoria, 2011; Menotti et al., 2007; Rajavel, 2010; Gonzalez an Woos, 2007) r s= T ( r) = ( L 1) p ( w) w (4) 0 r p s p r r s ( ) = r ( ) (5) s Laws masks texture analysis: Image texture exhibits spatial information regaring the intensity or color of the image (Tuceryan an Jain, 1998; Davies, 2008). Statistical techniques such as Law s mask, ege etection an Grey level co-occurrence matrix (Clausi, 2002) are popular because these methos are simple to be applie (Bharati et al., 2004) into the image processing proceure. Derive from the concept of texture energy efine at each pixel after a series of particular convolution with selecte mask, Laws Mask can prouce the texture energy measurement for the analysis of the

3 Res. J. App. Sci. Eng. Technol., 7(19): , 2014 texture property of each pixel (Rachii et al., 2008a, b). The two 2-imensioanl convolution kernels, generate from ifferent combinations of the 5 masks, are applie onto the converte gray scale image, which comprises of four ROIs. The image I i, j is assume to have a size of N rows an M columns. After the filtration, say by a 2D mask L 5 L 5, the image is recognize as a texture image, T 1 an will have an image imension of N+1 M+1. The texture image is escripte in Eq. (6): TL 5L5 i, j 5L = I ( L 5) (6) Accoring to Laws, only texture image TI 5 I 5 an can normalize the contrast of all texture image TI ij because these two 2D mask combinations have zero mean compare to other (Rachii, 2008). As a result, the result prouce by these mask combination will epen heavily on image intensity rather than texture. We will perform the winowing operation. The image passes through the Texture Energy Measure (TEM) to replace every pixel of the image by comparing the pixel with its local neighborhoo an subsequently sum up the absolute values from the neighboring pixel. The operation will lea to the creation of TEM image an escripte in Eq. (7): Table 3: General confusion matrix use in statistical valiation for various types of experiments Preicte Negative Positive Actual Negative a b Positive c et al., 2008; Daxin et al., 2010). Cluster analysis enables us to ientify the relationship between ifferent clusters. The Eucliean istance base clustering represents the grouping of a set of ata base on the istance measurement as shown (Danielsson, 1980). The efinition of Eucliean istance measurement is illustrate in Eq. (10), where the x i is the mean value of testing input image an x is the stanar mean value of the training set images. For instance, two ata point is compute using square root of the sum of the squares for the ifference between these two corresponing values base on the Pythagoras theorem, which stuie the geometry relationship between the sies of a triangle: 2 ( x, x) = ( x x) (10) i n i= 1 i 7 7 TEM = TI (7) i, j u= 7v= 7 ( i+ u, j+ v) After the winowing operation, we will normalize the features for the contrast of all the obtaine images in orer to be presente appropriately as image. All convolution kernels are zero-mean except for L 5 L 5, where the normalize kernels are escripte in Eq. (8): TI( i, j) mask Normalize ( TI mask ) = (8) TI ( i, j) L L Lastly, we will combine the TEM escriptors to eliminate a imensionality bias from features. Because TEM L5E5 been foun to be sensitive to changes in vertical eges while TEM E5L5 is sensitive to changes in horizontal eges, a single feature that is sensitive to simple ege content coul be obtaine by aing these two escriptors. The mathematical operations for the aitional of these two escriptors are compute in Eq. (9): TR E5L5 TEM L5E5+ TEME5L5 = (9) 2 Eucliean istance-base clustering: We have conucte cluster analysis in pattern recognition stage. Cluster analysis focuses primarily on assigning ifferent set of objects with certain egree of similarities into their assigne group (cluster) (Zhan-Ao In our experiment, we assume the output istance that is shorter than our threshol istance to be the same cluster an the classification is ivie into two classes i.e., healthy an osteoporosis. Thus, an image is classifie as healthy if the istance value compute is smaller compare to that of the osteoporosis bone image. We repeat the proceure when measuring the stanar eviation escriptor. Nevertheless, the mean value of healthy bone image is lower than osteoporosis image while in the case of stanar eviation, the osteoporosis image will rener a higher value compare to healthy image. The higher stanar eviation emonstrate by osteoporosis image is largely ue to the reason that the pixel value of osteoporosis image is lower, contributing to larger eviation from its mean value. Confusion matrix: Conventional reliability of the evelope system has been etermine by using the simple yet unreliable classification metric such as accuracy. For instance, the accuracy fails in the presence of large number of varying samples erive from ifferent classes. The confusion matrix, on the other han, offers much etaile statistical analysis on the true reliability of the system. The matrix iscloses the number of actual results that match the preicte results. There are two main elements that mae up of the confusion matrix table i.e., actual an preicte classes with 4 possible outcomes. Table 3 epicts the general formulation of confusion matrix. In particular, the terms

4 negative an positive refer to the ecision prouce by Law s Mask, where a in the general confusion matrix inicates correct preiction that an instance is positive, b inicates the number of incorrect preictions that an instance is negative, c inicates the number of incorrect preictions that an instance is positive an inicates the number of correct preictions that an instance is negative. There are four outcomes which are efine within the context of confusion matrix, commonly known as True Positive (TP), True Negative (TN), False Positive (FP) an False Negative (FN) (Hong Seng an Tian Swee, 2013a). The mathematical expressions for all four possible outcomes are illustrate in Eq. (11) to (14). If the positive an negative preictions are correctly matche by the actual incients, that the situation is recognize as true positive an true negative, respectively. In the case where the actual class is a yes but the preicte class has been wrongly classifie as no, then the result is consiere false negative. The same case applies for false positive: Res. J. App. Sci. Eng. Technol., 7(19): , 2014 TP= (11) c+ a TN = (12) b b TP= (13) b c TP = (14) c+ Meanwhile, the precision an the accuracy of the system can be compute through confusion matrix as well, in which the mathematical expression of these two elements are illustrate in Eq. (15) an (16): Accuracy = (15) b+ c+ a Pr ecision = (16) c RESEARCH ON LAW S MASK TEXTURE ANALYSIS SYSTEM RELIABILITY BASED ON CONFUSION MATRIX In 1986, a new texture measure that is relate to a set of orientate sensitive winows is evelope. The objective of his stuy has been focusing on evaluating the non-uniformity of the intensity within a set of locally irecte neighborhoo surrouning a pixel an followe by the first orer statistic computational of the variance measure within a large winows. Besies, 4005 Table 4: Confusion matrix for mean statistical analysis with the accuracy an precision compute using above mentione mathematical equation Preicte Positive Negative Actual Positive 15 9 Negative 3 9 TP TN FP FN 62.5% 75% 25% 37.5% The latter four statistical results represent the four possible outcomes i.e., TP, TN, FP, FN Table 5: Confusion matrix for stanar eviation statistical analysis with the accuracy an precision compute using above mentione mathematical equation Preicte Positive Negative Actual Positive 21 3 Negative 2 10 TP TN FP FN 87.5% 83.33% 16.67% 12.5% The latter four statistical results represent the four possible outcomes i.e., TP, TN, FP, FN global orientation sensitivities of the iniviual texture measures were consiere an results in a better outcome. However, the shortcoming of this metho lies with the limite number of texture samples. In the following year, the use of aaptive mask is suggeste instea of a fixe mask convolution. Base on his suggestion that the mask coul be tune in search of a better feature, he inclue the learning mechanism. Although the metho improves the reliability of the Laws mask texture consierably, the introuction of learning mechanism invariably increase the computational complexity when ajusting the mask elements (Srinivasan an Shobha, 2008) slowing own the whole etection system. The confusion matrix approach has been introuce in orer to assess the reliability of the texture analysis system for osteoporosis etection. In this stuy, we treat positive as healthy image class while negative as osteoporosis image class. The results are compare by using the mean an stanar eviation statistical analysis in orer to etermine a suitable statistical analysis escriptor for the system. The ecision is base on the accuracy an precision obtaine from the system. From Table 4, the recore reliability of the system base on mean statistical analysis is renering meiocre assessment result. The system is capable of recognizing the osteoporosis image by 50% an healthy image by 83.33% while the accuracy of the system stans at 66.67%. A much etaile statistical analysis inicate the capability of mean statistical escriptor to ifferentiate healthy an osteoporosis images stans at 62.5% in classifying the healthy images an 75% in classifying the osteoporosis images. Meanwhile, from Table 5, the stanar eviation statistical analysis is consiere as a better selection with the precision of ientifying healthy images of

5 91.30% an osteoporosis of 76.92%. The accuracy of the overall system, obtaine by combining both true positive an true negative ata, is 86.11%. We have observe the high reliability of recognizing the correct healthy images where 87.5% of the healthy images are being classifie correctly. Furthermore, 83.33% of the osteoporosis images have been correctly ientifie. The stanar eviation statistical analysis improves the classification statistical escriptor for the system in both the healthy an osteoporosis cases. Quantitatively, the TN an TF for the stanar eviation statistical escriptor is an 87.5% against 75 an 62.5%, respectively for mean statistical escriptor. While the TN an TF for stanar eviation statistical escriptor is higher, both statistical escriptors are renering consierably satisfying results for this stage of experiment. The relatively high TN an TF rates using mean statistical escriptor an stanar eviation statistical escriptor in classifying the healthy an osteoporosis images inicate the true potential of Laws mask technique to irectly analyze the texture of bone through X-ray images. Moreover, our experiment contributes to ientifying the more appropriate statistical escriptor in classification of the texture analysis of musculoskeletal stuy (Yan Chai et al., 2013; Hong Seng an Tian Swee, 2013a, b) as few experiments have been eicate to the statistical valiation of the texture analysis system. CONCLUSION In this stuy, we focus on the reliability computational of the system. In orer to assess the reliability of the overall texture analysis system for osteoporosis etection, we have emonstrate a etaile statistical escription using confusion matrix. The mean statistical escriptor has been emonstrating lower overall accuracy an precision in ientifying both the healthy an osteoporosis images compare with stanar eviation statistical escriptor. Our finings suggest the application of the stanar eviation statistical escriptor in future researches for eveloping the Law s Mask technique for osteoporosis etection. Besies, we also recommen that future researchers nee to increase the number of images inclue in their experiment as this is currently a rawbacks in our experiment. Furthermore, more statistical analysis parameters such as sensitivity, specificity, entropy, skewness an kurtosis shoul be inclue in future research for eveloping the texture analysis for osteoporosis prevention. ACKNOWLEDGMENT The authors gratefully acknowlege the Ainnuin Wahi Scholarship provie by Universiti Teknologi Malaysia an research grant provie by Research Res. J. App. Sci. Eng. Technol., 7(19): , Management Centre sponsore by Ministry of Higher Eucation, Malaysia. Vot: Q.J H41, GUP Universiti Teknologi Malaysia, Johor Bahru, Malaysia in supporting this stuy. REFERENCES Bauer, J.S. an T.M. Link, Avances in osteoporosis imaging. Eur. J. Raiol., 71: Bharati, M.H., J.J. Liu an J.F. MacGregor, Image texture analysis: Methos an comparisons. Chemometr. Intell. Lab., 72: Chauhan, R. an S.S. Bhaoria, An improve image contrast enhancement base on histogram equalization an brightness preserving weight clustering histogram equalization. Proceeing of the International Conference on Communication Systems an Network Technologies (CSNT). Katra, Jammu, pp: Clausi, D.A., An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens., 28: Danielsson, P.E., Eucliean istance mapping. Comput. Vision Graph., 14: Davies, E.R., Introuction to Texture Analysis. In: Mirmehi, M., X. Xie an J. Suri (Es.), Hanbook of Texture Analysis. Imperial College Press, pp: Daxin, T., W. Yunpeng, L. Guangquan an Y. Guizhen, A VANETs routing algorithm base on Eucliean istance clustering. Proceeing of the International Conference on Future Computer an Communication (ICFCC), pp: V1-183-V Eastell, R., Osteoporosis. Meicine, 33: Gonzalez, R.C. an R.E. Woos, Digital Image Processing. 3r En., Prentice Hall, NY. Hong Seng, G. an T. Tian Swee, 2013a. Osteoarthritis etection system using optimal ynamic feature configuration. Int. J. Circ. Syst. Signal Process., 7: Hong Seng, G. an T. Tian Swee, 2013b. Spectral coefficient system for osteoarthritis etection. Int. J. Circ. Syst. Signal Process., 7: Laws, K.L., Texture image segmentation. Ph.D. Thesis, University of South California, Los Angeles, California. Menotti, D., L. Najman, J. Facon an A.A. e Araujo, Multi-histogram equalization methos for contrast enhancement an brightness preserving. IEEE T. Consum. Electr., 53: Rachii, K.L., Rapi texture ientification. Proceeing of the SPIE Conference on Image Processing for Missile Guiance, pp: Rachii, M., C. Chappar, A. Marchaier, C. Gaois, E. Lespessailles an C.L. Benhamou, 2008a. Application of Laws' masks to bone texture analysis: An innovative image analysis tool in osteoporosis. Proceeing of the 5th IEEE International Symposium on Biomeical Imaging: From Nano to Macro, pp:

6 Res. J. App. Sci. Eng. Technol., 7(19): , 2014 Rachii, M., C. Chappar, A. Marchaier, C. Gaois, E. Lespessailles an C.L. Benhamou, 2008b. Laws masks escriptors applie to bone texture analysis: An innovative an iscriminant tool in osteoporosis. Skeletal Raiol., 37: Rajavel, P., Image epenent brightness preserving histogram equalization. IEEE T. Consum. Electr., 56: Srinivasan, S.G. an G. Shobha, Statistical analysis. Proceeings of Worl Acaemy of Science, Engineering an Technology, 36: 6. Tuceryan, M. an A.K. Jain, Texture Analysis. In: Chen, C.H., L.F. Pau an P.S.P. Wang (Es.), The Hanbook of Pattern Recognision an Computer Vision. Worl Scientific Publishing Co., Hackensack, NJ, 2: Wang, C. an Y. Zhongfu, Brightness preserving histogram equalization with maximum entropy: A variational perspective. IEEE T. Consum. Electr., 51: Yan Chai, H., T. Tianswee., G. Hongseng. an L. Khinwee, Multipurpose contrast enhancement on epiphyseal plates an ossification centers for bone age assessment. Biome. Eng. Online, 12:1-19. Zhan-Ao, X., C. Feng an W. Li-Ping, A weighting fuzzy clustering algorithm base on eucliean istance. Proceeings of the th International Conference on Fuzzy Systems an Knowlege Discovery, October 18-20, 1:

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