A Model Optimization Approach to the Automatic Segmentation of Medical Images

Ahmed AFIFI  Toshiya NAKAGUCHI  Norimichi TSUMURA  Yoichi MIYAKE  

IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.4   pp.882-890
Publication Date: 2010/04/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.882
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
model fitting,  image segmentation,  kernel methods,  particle swarm,  shape priors,  

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The aim of this work is to develop an efficient medical image segmentation technique by fitting a nonlinear shape model with pre-segmented 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 efficiently 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.