Efficient liver segmentation in CT images based on graph cuts and bottleneck detection

Document Type

Article

Publication Date

11-1-2016

Abstract

Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.

Identifier

85002293087 (Scopus)

Publication Title

Physica Medica

External Full Text Location

https://doi.org/10.1016/j.ejmp.2016.10.002

e-ISSN

1724191X

ISSN

11201797

PubMed ID

27771278

First Page

1383

Last Page

1396

Issue

11

Volume

32

Grant

2015RS4008

Fund Ref

National Natural Science Foundation of China

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