Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume

被引：69

作者：

Nakagomi, Keita ^{[1
]}

Shimizu, Akinobu ^{[1
]}

Kobatake, Hidefumi ^{[1
]}

Yakami, Masahiro ^{[2
]}

Fujimoto, Koji ^{[2
]}

Togashi, Kaori ^{[2
]}

机构：

[1] Tokyo Univ Agr & Technol, Tokyo, Japan

[2] Kyoto Univ, Kyoto, Japan

来源：

MEDICAL IMAGE ANALYSIS | 2013年 / 17卷 / 01期

关键词：

Graph cuts; Multi-shape; Lung segmentation; Neighbor constraint; CT image;

D O I：

10.1016/j.media.2012.08.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a novel graph cut algorithm that can take into account multi-shape constraints with neighbor prior constraints, and reports on a lung segmentation process from a three-dimensional computed tomography (CT) image based on this algorithm. The major contribution of this paper is the proposal of a novel segmentation algorithm that improves lung segmentation for cases in which the lung has a unique shape and pathologies such as pleural effusion by incorporating multiple shapes and prior information on neighbor structures in a graph cut framework. We demonstrate the efficacy of the proposed algorithm by comparing it to conventional one using a synthetic image and clinical thoracic CT volumes. (c) 2012 Elsevier B.V. All rights reserved.

引用

页码：62 / 77

页数：16

共 27 条

[1] Fast approximate energy minimization via graph cuts [J].