Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume.
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.
Tokyo University of Agriculture and Technology, Tokyo, Japan.
Keita
K
Nakagomi
Akinobu
A
Shimizu
Hidefumi
H
Kobatake
Masahiro
M
Yakami
Koji
K
Fujimoto
Kaori
K
Togashi
D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008168:Lung / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D009929:Organ Size; D013902:Radiography, Thoracic / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*