A Model Optimization Approach to the Automatic Segmentation of Medical Images

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 (NO) to adapt the model parameters The proposed technique is fully automated is talented to deal with complex shape variations. can efficiently optimize die model to it 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 front computed tomography (CT) scans and the obtained results are very hopeful.