Performance evaluation of computer aided diagnostic tool (CAD) for detection of ultrasonic based liver disease.

Recent advances in digital imaging technology have greatly enhanced the interpretation of critical/pathology conditions from the 2-dimensional medical images. This has become realistic due to the existence of the computer aided diagnostic tool. A computer aided diagnostic (CAD) tool generally possesses components like preprocessing, identification/selection of region of interest, extraction of typical features and finally an efficient classification system. This paper enumerates on development of CAD tool for classification of chronic liver disease through the 2-D image acquired from ultrasonic device. Characterization of tissue through qualitative treatment leads to the detection of abnormality which is not viable through qualitative visual inspection by the radiologist. Common liver diseases are the indicators of changes in tissue elasticity. One can show the detection of normal, fatty or malignant condition based on the application of CAD tool thereby, further investigation required by radiologist can be avoided. The proposed work involves an optimal block analysis (64 x 64) of the liver image of actual size 256 x 256 by incorporating Gabor wavelet transform which does the texture classification through automated mode. Statistical features such as gray level mean as well as variance values are estimated after this preprocessing mode. A non-linear back propagation neural network (BPNN) is applied for classifying the normal (vs) fatty and normal (vs) malignant liver which yields a classification accuracy of 96.8%. Further multi classification is also performed and a classification accuracy of 94% is obtained. It can be concluded that the proposed CAD can be used as an expert system to aid the automated diagnosis of liver diseases.