3D α expansion and graph cut algorithms for automatic liver segmentation from CT images

Authors:
Elena Casiraghi;Gabriele Lombardi;Stella Pratissoli;Simone Rizzi
Affiliations:
Universita degli Studi di Milano, Computer Science Department, Milano, Italy;Universita degli Studi di Milano, Computer Science Department, Milano, Italy;Universita degli Studi di Milano, Computer Science Department, Milano, Italy;Universita degli Studi di Milano, Computer Science Department, Milano, Italy
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Year:
2007

Citing 5
Cited 1

Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Semiautomatic Segmentation of 3D Objects in Medical Images

MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
What Energy Functions Can Be Minimizedvia Graph Cuts?

IEEE Transactions on Pattern Analysis and Machine Intelligence
Liver Segmentation from CT Scans: A Survey

WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules

IEEE Transactions on Information Technology in Biomedicine

Liver segmentation in CT images for intervention using a graph-cut based model

MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abdominal CT images have been widely studied in the recent years as they are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, that is still an open problem. In this paper we describe an automatic method to segment the liver from abdominal CT data, by combining an α-expansion and a graph cut algorithm. When evaluated on the data of 40 patients, by comparing the automatically detected liver volumes to the liver boundaries manually traced by three experts, the method achieves a symmetric volume difference of 94%.