Title
Pulmonary nodule segmentation in thoracic 3D CT images integrating boundary and region information
Abstract
Accurately segmenting and quantifying pulmonary nodules structure is a key issue in three-dimensional (3-D) computer-aided diagnosis (CAD) schemes. This paper presents a segmentation approach of pulmonary nodules in thoracic 3-D images. This approach consists of two processes such as a pre-process for removing vessels attached and a surface deformation process. The pre-process is performed by 3-D gray-scale morphological operations. The surface deformation model used here integrates boundary and region information to deal with inappropriate position or size of an initial surface. This approach is derived through a 3-D extension of the geodesic active region model developed by Paragios and Deriche. First, in order to measure differences between the nodule and other regions a statistical analysis of the observed intensity is performed. Based on this analysis, the boundary and region information are represented by boundary and region likelihood, respectively. Second, an objective function is defined by integrating boundary and region-based segmentation modules. This integration aims at seeking surfaces that provide high boundary likelihood and high posterior segmentation probability. Finally, the deformable surface model is obtained by minimizing the objective function and, is implemented by a level set approach. We demonstrate an advantage of the proposed segmentation approach in comparison with the conventional deformable surface model using a practical 3-D pulmonary image.
Year
DOI
Venue
2003
10.1117/12.480664
Proceedings of SPIE
Keywords
Field
DocType
pulmonary nodule,segmentation,vessel removal,surface deformation
CAD,Computer vision,Market segmentation,Scale-space segmentation,Segmentation,Computer-aided diagnosis,Level set,Artificial intelligence,Geography,Geodesic,Statistical analysis
Conference
Volume
ISSN
Citations 
5032
0277-786X
1
PageRank 
References 
Authors
0.36
0
10
Name
Order
Citations
PageRank
Yoshiki Kawata119254.44
Noboru Niki218866.10
Hironobu Ohmatsu313845.23
masahiko kusumoto44616.28
Ryutaro Kakinuma59724.90
Kiyoshi Mori64710.84
hiroyuki nishiyama74611.62
Kenji Eguchi812942.78
Masahiro Kaneko95519.24
Noriyuki Moriyama1014850.47