SHAPE PRIOR SEGMENTATION OF MEDICAL IMAGES USING PARTICLE SWARM OPTIMIZATION

The image segmentation is the first and essential process in many medical applications. This process is traditionally performed by radiologists or medical specialists to manually trace the objects on each image. In almost all of these applications, the medical specialists have to access a large number of images which is a tedious and a time consuming process. On the other hand, the automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. In this work, we propose a fully automated medical image segmentation framework. In this framework, the segmentation process is constrained by two prior models; a shape prior model and a texture prior model. The shape prior model is constructed from a set of manually segmented images using the principle component analysis (PCA) while the wavelet packet decomposition is utilized to extract the texture features. The fisher linear discriminate algorithm is employed to build the texture prior model from the set of texture features and to perform a preliminary segmentation. Furthermore, the particle swarm optimization algorithm (PSO) is used to refine the preliminary segmentation according to the shape prior model. In this work, we tested the proposed technique for the segmentation of the liver from abdominal CT scans and the obtained results show the efficiency of the proposed technique to accurately delineate the desired objects.