Deformable Registration of Biomedical Images Using 2D Hidden Markov Models

Segmentation of MRI brain scans using spatial constraints and 3D features

This paper presents a novel unsupervised algorithm for brain tissue segmentation in magnetic resonance imaging (MRI). The proposed algorithm, named Gardens2, adopts a clustering approach to segment voxels of a given MRI into three classes: cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using an overlapping criterion, 3D feature descriptors and prior atlas information, Gardens2 generates a segmentation mask per class in order to parcellate the brain tissues. We assessed our method using three neuroimaging datasets: BrainWeb, IBSR18, and IBSR20, the last two provided by the Internet Brain Segmentation Repository. Its performance was compared with eleven well established as well as newly proposed unsupervised segmentation methods. Overall, Gardens2 obtained better segmentation performance than the rest of the methods in two of the three databases and competitive results when its performance was measured by class. Graphical Abstract Brain tissue segmentation using 3D features and an adjusted atlas template.

Incorporation of Structural Tensor and Driving Force Into Log-Demons for Large-Deformation Image Registration

Large-deformation image registration is important in theory and application in computer vision, but is a difficult task for non-rigid registration methods. In this paper, we propose a structural Tensor and Driving force-based Log-Demons algorithm for it, named TDLog-Demons for short. The structural tensor of an image is proposed to obtain a highly accurate deformation field. The driving force is proposed to solve the registration issue of large-deformation that often causes Log-Demons to trap into local minima. It is defined as a point correspondence obtained via multisupport-region-order-based gradient histogram descriptor matching on image's boundary points. It is integrated into an exponentially decreasing form with the velocity field of Log-Demons to move the points accurately and to speed up a registration process. Consequently, the driving force-based Log-Demons can well deal with large-deformation image registration. Extensive experiments demonstrate that the TDLog-Demons not only captures large deformations at a high accuracy but also yields a smooth deformation.