Coupled nonparametric shape and moment-based intershape pose priors for multiple basal ganglia structure segmentation.

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.

Scanning from heating: 3D shape estimation of transparent objects from local surface heating.

Today, with quality becoming increasingly important, each product requires three-dimensional in-line quality control. On the other hand, the 3D reconstruction of transparent objects is a very difficult problem in computer vision due to transparency and specularity of the surface. This paper proposes a new method, called Scanning From Heating (SFH), to determine the surface shape of transparent objects using laser surface heating and thermal imaging. Furthermore, the application to transparent glass is discussed and results on different surface shapes are presented.

Robustness of local binary patterns in brain MR image analysis.

The aging population in developed countries has shifted considerable research attention to diseases related to age. Because age is one of the highest risk factors for neurodegenerative diseases, the need for automated brain image analysis has significantly increased. Magnetic Resonance Imaging (MRI) is a commonly used modality to image brain. MRI provides high tissue contrast; hence, the existing brain image analysis methods have often preferred the intensity information to others, such as texture. Recently, an easy-to-compute texture descriptor, Local Binary Pattern (LBP), has shown promise in various applications outside the medical field. In this paper, after extensive experiments, we show that rotation-invariant LBP is invariant to some common MRI artifacts that makes it possible to use it in various high-level brain MR image analysis applications.