Research on CT Image Segmentation of Computer-Aided Liver Operation

Abstract

The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. Generally, liver segmentation methods are divided into two main classes, semi-automatic and fully automatic methods, under each of these two categories, several methods, approaches, related issues and problems will be defined and explained. The evaluation measurements and scoring for the liver segmentation are shown, followed by the comparative study for liver segmentation methods, pros and cons of methods will be accentuated carefully. Here a fully 3D algorithm for automatic liver segmentation from CT volumetric datasets is presented. The algorithmstarts by smoothing the original volume using anisotropic diffusion. The coarse liver region is obtained from the threshold process that is based on a priori knowledge.

Empirical greedy machine‐based automatic liver segmentation in CT images

“…Liver segmentation remains difficult due to the low contrast between the liver and surrounding tissues, blurry borders, neighbouring vessels that have different appearances and heterogeneous density pathologies. Furthermore, different liver shapes, sizes, and positions make automatic segmentation more difficult [9]. Manual extraction of the liver tissue remains the primary technique performed by physicians but this process takes time and is based on subjective judgment.…”

Feature Learning Based Random Walk for Liver Segmentation

“…This method is indispensable for volume measurement, functional assessment, lesion location and operation planning [4]. The shape and size of the liver remarkably differ among individuals.…”