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1 2011

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Liver Tumour Classification Using Co-Occurrence Matrices on the Contourlet Domain

Kumar S. S., Moni R. S., Rajeesh J.

Machine Graphics and Vision

2011

Vol. 20, No. 2

197-214

Liver disease is one of the most common diseases around the world, seriously affecting the health of humans. Computed tomography image based Computer Aided Diagnosis (CAD) could be crucially important in supporting liver cancer diagnosis. An effective approach to realize a CAD system for this purpose is described in this work. The CAD system employs automatic tumour segmentation, texture feature extraction and characterization into malignant and benign tumours. A Region of Inter- est (ROI) cropped from the automatically segmented tumour by confidence connected region growing and alternative fuzzy c means clustering is decomposed using multiresolution and multidirectional con- tourlet transform to obtain contourlet coefficients. Co-occurrence matrices of the contourlet coefficients are determined, and six parameters of texture characteristics, which include Angular Second Moment, Contrast, Correlation, Inverse Difference Moment, Entropy and Variance, are extracted from them. The extracted feature sets are classified into benign and malignant by a Generalized Regression Neural Net- work (GRNN) classifier. The performance of this scheme is evaluated by various performance measures and by the use a of the Receiver Operating Characteristic (ROC) curve. The results are compared with those obtained by a similar system using Wavelet Coefficients co-occurrence Matrix (WCCM) and Gray Level co-occurrence Matrix (GLCM) texture features. The results indicate that the proposed scheme based on the CCCM texture is effective for classifying malignant and begin liver tumours in abdominal CT imaging.