Computer Aided Diagnosis from CT Images Using Wavelet Features with Progressive Classification

Computer aided diagnostic system for classification of normal and infected tissue from computed tomography (CT) images of abdomen is presented. The region of interest (ROI) is segmented from CT images of normal tissues, cysts, stones and tumors using active contour models. The ROIs are fed to the next stage that is feature extraction stage. In the feature extraction stage, wavelet features are extracted from segmented ROIs. In this paper, five different features are used to form feature vectors. The classification is done by using the concept of progressive classification. The classification of input query image is done progressively by eliminating unlikely ROIs from the data set till only one ROI is left. The class of the left over ROI is assigned to the unknown ROI. The results show that CAD presented in this paper achieves a classification accuracy of 87.5% which is much better than the conventional texture extraction and classification systems.

[1]  G..  A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[2]  Παντελής Α. Ασβεστάς,et al.  A power differentiation method of fractal dimension estimation for 2-D signals , 2015 .

[3]  Jacek M. Zurada,et al.  Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images , 1996, IEEE Trans. Medical Imaging.

[4]  Pau-Choo Chung,et al.  Classification of liver diseases from CT images using BP-CMAC neural network , 2005, 2005 9th International Workshop on Cellular Neural Networks and Their Applications.

[5]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[6]  Ingrid Daubechies.  Different Perspectives on Wavelets , 2016 .

[7]  Yung-Chang Chen,et al.  Texture features for classification of ultrasonic liver images , 1992, IEEE Trans. Medical Imaging.

[8]  E-Liang Chen,et al.  An automatic diagnostic system for CT liver image classification , 1998, IEEE Transactions on Biomedical Engineering.

[9]  Lucia Dettori,et al.  A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography , 2007, Comput. Biol. Medicine.

[10]  Turgay Çelik,et al.  Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands , 2011, Comput. Electr. Eng..

[11]  Yung-Nien Sun,et al.  Ultrasonic image analysis for liver diagnosis , 1996 .

[12]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Ruey-Feng Chang,et al.  Computer-aided diagnosis of liver tumors on computed tomography images , 2017, Comput. Methods Programs Biomed..

[14]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..