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15 May 2003 Pulmonary nodule segmentation in thoracic 3D CT images integrating boundary and region information
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yoshiki Kawata, Noboru Niki, Hironobu Ohamatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, and Noriyuki Moriyama "Pulmonary nodule segmentation in thoracic 3D CT images integrating boundary and region information", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480664
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