Shape and Texture Priors for Liver Segmentation in Abdominal Computed Tomography Scans Using the Particle Swarm Optimization Algorithm

Accurate medical diagnosis requires the segmentation of a large number of medical images. Although manual segmentation provides good results, it is a costly process in terms of both money and time. Automatic segmentation, on the other hand, remains a challenge due to low image contrast and ill-defined boundaries. In this report, we propose a fully automated medical image segmentation framework in which 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 principal component analysis (PCA), while wavelet packet decomposition is used to extract the texture features. The Fisher linear discriminant algorithm is employed to build the texture prior model from the set of texture features and to perform preliminary segmentation. Then, the particle swarm optimization (PSO) algorithm is used to refine the preliminary segmentation according to the shape prior model. In this work, we tested the efficacy of the proposed technique for segmentation of the liver in abdominal CT scans. The obtained results demonstrated the efficiency of the proposed technique in accurately delineating the target objects.