Level Set-based CT Liver Computer Aided Diagnosis System

Abstract

All CT Liver Computer Aided Diagnosis (CAD) system have an objective of giving a hand to radiologist to aid in detecting the lesions. The objective of this paper is to evaluate a new combined approach intended for reliable CT liver image segmentation to separate the liver from other organs and segment the liver into set of regions of interest (ROIs). Three main phases are processed in CAD systems including image filtering, segmentation and classification. The used approach combines the level set with watershed approach used as post segmentation step to produce a reliable segmentation result. Features of first order statistics and grey-level co-occurrence matrix, are calculated and passed to an artificial neural network to be trained and classify lesion ROIs. Filtering is used before the segmentation approach to enhance contrast, remove noise and emphasize certain features as well as connecting ribs around the liver. To evaluate the performance of presented approach, we present tests on different CT liver images. The experimental results obtained, show that the overall accuracy offered by the proposed approach is 92.1% in segmenting CT liver images into set of regions even with noise, and 88.9% average accuracy for neural network classification.