Level set-based CT liver image segmentation with watershed and artificial neural networks

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 a set of regions of interest (ROIs). The 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 cooccurrence matrix, are calculated and passed to an artificial neural network, to be trained and to classify infected regions. 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 performed many 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.