A fully automatic and efficient technique for liver segmentation from abdominal CT images
Nader H. Abdel-massieh, Mohiy Mohammed Hadhoud, Kamel A. Moustafa · International Conference on Informatics and Systems · 2010
As a mean for liver investigation, abdominal CT images have been widely studied in the recent years. Processing CT image includes the automatic diagnosis of liver pathologies, such as detecting lesions and following vessels ramification, and its 3D volume rendering. The first step in all these studies is the automatic liver segmentation. This paper presents a fully automatic method to segment the liver from abdominal CT data with no interaction from user. A statistical model-based approach is used to distinguish roughly liver tissue from other abdominal organs. It is followed by applying force-driven optimized active contour (snake) in order to obtain a smoother and finer liver contour. The new segmentation technique has been evaluated on fifteen datasets, by comparing the automatically detected liver contour to the liver boundaries manually traced by an expert. Tests are reported on 15 datasets and promising result shows that sensitivity and specificity for automatic liver segmentation are 95% and 99% respectively.