Region Growing Segmentation with Iterative K-means for CT Liver Images

Abstract

In this paper, it is intended to enhance the simple region growing technique (RG) to extract liver from the abdomen away from other organs in CT images. Iterative K-means clustering technique is used as a preprocessing step to pass the image to region growing and watershed segmentation techniques. The usage of K-means and region growing is preferred here for its simplicity and low cost of execution. The proposed approach starts with cleaning the annotation and enhancing the boundaries of the liver. This is performed using texture filter and ribs connection algorithm, followed by iterative K-means. K-means removes the clusters with higher intensity values. Then region growing is used to separate the whole liver. Finally, comes the role of watershed that divides the liver into a number of regions of interest (ROIs). The experimental results show that the overall accuracy offered by the proposed approach, results in 92.38% accuracy.