Sorted self-similarity for multi-modal image registration.

In medical image analysis, registration of multimodal images has been challenging due to the complex intensity relationship between images. Classical multi-modal registration approaches evaluate the degree of the alignment by measuring the statistical dependency of the intensity values between images to be aligned. Employing statistical similarity measures, such as mutual information, is not promising in those cases with complex and spatially dependent intensity relations.

Multi-modal image registration using structural features.

Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing the complex phase and gradient-based information.

A hybrid hierarchical approach for brain tissue segmentation by combining brain atlas and least square support vector machine.

In this paper, we present a new semi-automatic brain tissue segmentation method based on a hybrid hierarchical approach that combines a brain atlas as a priori information and a least-square support vector machine (LS-SVM). The method consists of three steps. In the first two steps, the skull is removed and the cerebrospinal fluid (CSF) is extracted.