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Proceedings Paper

Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors
Author(s): Ahmed Afifi; Toshiya Nakaguchi; Norimichi Tsumura
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Paper Abstract

In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

Paper Details

Date Published: 13 March 2010
PDF: 10 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 762349 (13 March 2010); doi: 10.1117/12.844378
Show Author Affiliations
Ahmed Afifi, Chiba Univ. (Japan)
Toshiya Nakaguchi, Chiba Univ. (Japan)
Norimichi Tsumura, Chiba Univ. (Japan)


Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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