SVM-Based CAC System for B-Mode Kidney Ultrasound Images

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee’s sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.

[1]  Adel Al-Jumaily,et al.  Feature subset selection using differential evolution and a statistical repair mechanism , 2011, Expert Syst. Appl..

[2]  M. Madheswaran,et al.  Ultrasound Kidney Image Analysis for Computerized Disorder Identification and Classification Using Content Descriptive Power Spectral Features , 2007, Journal of Medical Systems.

[3]  Jitendra Virmani,et al.  SVM-Based Characterization of Liver Ultrasound Images Using Wavelet Packet Texture Descriptors , 2013, Journal of Digital Imaging.

[4]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[5]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Vinod Kumar,et al.  Enhancement of the ultrasound images by modified anisotropic diffusion method , 2010, Medical & Biological Engineering & Computing.

[7]  Scott T. Acton,et al.  Speckle reducing anisotropic diffusion , 2002, IEEE Trans. Image Process..

[8]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Vinod Kumar,et al.  A rapid approach for prediction of liver cirrhosis based on first order statistics , 2011, 2011 International Conference on Multimedia, Signal Processing and Communication Technologies.

[10]  Fayyaz ul Amir Afsar Minhas,et al.  Automated Classification of Liver Disorders using Ultrasound Images , 2012, Journal of Medical Systems.

[11]  M. Madheswaran,et al.  Evaluation of Tissue Characteristics of Kidney for Diagnosis and Classification Using First Order Statistics and RTS Invariants , 2007, 2007 International Conference on Signal Processing, Communications and Networking.

[12]  M. Madheswaran,et al.  A General Segmentation Scheme for Contouring Kidney Region in Ultrasound Kidney Images using Improved Higher Order Spline Interpolation , 2008 .

[13]  Vinod Kumar,et al.  Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound , 2013 .

[14]  Jiuqing Wan,et al.  Features extraction based on wavelet packet transform for B-mode ultrasound liver images , 2010, 2010 3rd International Congress on Image and Signal Processing.

[15]  Vinod Kumar,et al.  Pca-SVm based caD System for Focal liver lesions using B-mode ultrasound Images , 2013 .

[16]  Vinod Kumar,et al.  Neural network based focal liver lesion diagnosis using ultrasound images , 2011, Comput. Medical Imaging Graph..

[17]  Jitendra Virmani,et al.  Prediction of cirrhosis from liver ultrasound B-mode images based on Laws' masks analysis , 2011, 2011 International Conference on Image Information Processing.

[18]  M. Madheswaran,et al.  Analysis of Ultrasound Kidney Images Using Content Descriptive Multiple Features for Disorder Identification and ANN Based Classification , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[19]  Dar-Ren Chen,et al.  Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines , 2006, Neural Computing & Applications.

[20]  Jitendra Virmani,et al.  Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound , 2013, Journal of Digital Imaging.

[21]  M Glavin,et al.  Echocardiographic speckle reduction comparison , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[22]  N. Kumaravel,et al.  A Comparison of Wavelet, Curvelet and Contourlet based Texture Classification Algorithms for Characterization of Bone Quality in Dental CT , 2011 .

[23]  M. Madheswaran,et al.  Quantitative and Qualitative Evaluation of US Kidney Images for Disorder Classification using Multi-Scale Differential Features , 2007 .

[24]  T R Crimmins,et al.  Geometric filter for speckle reduction. , 1985, Applied optics.

[25]  Jitendra Virmani,et al.  Prediction of Cirrhosis Based on Singular Value Decomposition of Gray Level Co-occurence Marix and aNneural Network Classifier , 2011, 2011 Developments in E-systems Engineering.

[26]  Alexander A. Sawchuk,et al.  Adaptive restoration of images with speckle , 1987, IEEE Trans. Acoust. Speech Signal Process..

[27]  Carl-Fredrik Westin,et al.  Oriented Speckle Reducing Anisotropic Diffusion , 2007, IEEE Transactions on Image Processing.

[28]  Jason Weston,et al.  A user's guide to support vector machines. , 2010, Methods in molecular biology.

[29]  Chien-Cheng Lee,et al.  Gabor Wavelets and SVM Classifier for Liver Diseases Classiflcation from CT Images , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[30]  Zohreh Azimifar,et al.  Contourlet-Based Mammography Mass Classification , 2007, ICIAR.

[31]  Santiago Aja-Fernández,et al.  On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering , 2006, IEEE Transactions on Image Processing.

[32]  M. Madheswaran,et al.  A Hybrid Fuzzy-Neural System for Computer-Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features , 2008, Journal of Medical Systems.

[33]  S. Nawaz,et al.  Hepatic lesions classification by ensemble of SVMs using statistical features based on co-occurrence matrix , 2008, 2008 4th International Conference on Emerging Technologies.

[34]  W. Moon,et al.  Computer‐aided diagnosis using morphological features for classifying breast lesions on ultrasound , 2008, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[35]  Jitendra Virmani,et al.  Neural Network Ensemble Based CAD System for Focal Liver Lesions from B-Mode Ultrasound , 2014, Journal of Digital Imaging.

[36]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[37]  Jitendra Virmani,et al.  SVM-based characterisation of liver cirrhosis by singular value decomposition of GLCM matrix , 2013, Int. J. Artif. Intell. Soft Comput..

[38]  Jong-Sen Lee,et al.  Digital image smoothing and the sigma filter , 1983, Comput. Vis. Graph. Image Process..

[39]  Konstantina S. Nikita,et al.  Multiscale geometric texture analysis of ultrasound images of carotid atherosclerosis , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[40]  M. Madheswaran,et al.  Texture pattern analysis of kidney tissues for disorder identification and classification using dominant Gabor wavelet , 2010, Machine Vision and Applications.