Wolf Local Thresholding Approach for Liver Image Segmentation in CT Images

Abstract

This paper enhances the usage of level set method to get a reliable liver image segmentation in CT images. The approach depends on a preprocessing phase to enhance the liver’s edges. This phase is performed in two ways using the morphological operations and wolf local thresholding. The first way starts with applying the morphological operations on the image to clean image annotation and bed lines. Then, it applies contrast stretching and texture filters. The other way applies the wolf local threshold to each point in the image. It uses a window or a mask to calculate the average and standard deviation to apply an iterative threshold. Each way is followed by a step of connecting ribs to separate the flesh and skin from liver’s region. The last step is to use level set method to segment the whole liver. A set of 47 images taken in pre-contrast phase, were used to test the approach. Validating the approach is done using similarity index measure. The obtained experimental results showed that the overall accuracy presented by the proposed approach results in 93.19 % accuracy for using morphological operations, and 93.30 % accuracy for using Wolf local thresholding.