A Model Optimization Approach to the Automatic Segmentation of
Medical Images

Ahmed AFIFI, , Toshiya NAKAGUCHI, Norimichi TSUMURA, and Yoichi MIYAKE

Chiba University,

To appeare in IEICE Trans. on Information and Systems, Vol.E93-D, No.4, pp.882-889, Apr. 2010

The aim of this work is to develop an ecient medical image segmentation technique by fitting a nonlinear shape model with presegmented images. In this technique, the kernel principle component analysis (KPCA) is used to capture the shape variations and to build the nonlinear shape model. The pre-segmentation is carried out by classifying the image pixels according to the high level texture features extracted using the over-complete wavelet packet decomposition. Additionally, the model fitting is completed using the particle swarm optimization technique (PSO) to adapt the model parameters. The proposed technique is fully automated, is talented to deal with complex shape variations, can eciently optimize the model to fit the new cases, and is robust to noise and occlusion. In this paper, we demonstrate the proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans and the obtained results are very hopeful.

[PDF] (To appeare)