Liver tumor diagnosis by gray level and contourlet coefficients texture analysis

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 tumor segmentation, texture feature extraction and characterization into malignant and benign tumors. A Region of Interest (ROI) cropped from the automatically segmented tumor by confidence connected region growing and alternative fuzzy c means clustering is decomposed using multiresolution and multidirectional contourlet transform to obtain contourlet coefficients. Both first order statistic and second order statistic features are extracted from the gray level and contourlet detail coefficients. The extracted feature sets are classified by a Probabilistic Neural Network (PNN) classifier into benign and malignant. The system is evaluated by using different performance measures and the results indicate that the contourlet coefficient texture is effective for classifying malignant and benign liver tumors from abdominal CT imaging.