Brain Image Segmentation with Gradient Information

Image Segmentation with a Sobolev Gradient Method

The most effective methods for finding object boundaries in a digital image involve minimizing a functional over a set of curves or surfaces, where the functional includes internal energy terms for regularization and external energy terms that allign the curves or surfaces with object boundaries. Current practice is to seek critical points of the energy functional by what amounts to a steepest ...

Minimizing Loss of Information at Competitive PLIP Algorithms for Image Segmentation with Noisy Back Ground

In this paper, two training systems for selecting PLIP parameters have been demonstrated. The first compares the MSE of a high precision result to that of a lower precision approximation in order to minimize loss of information. The second uses EMEE scores to maximize visual appeal and further reduce information loss. It was shown that, in the general case of basic addition, subtraction, or mul...

Modulated Intensity Gradient and Texture Gradient Based Image Segmentation

Image segmentation is currently a prominent topic for both military and commercial researchers. This paper concentrates on the image segmentation based on the combination of modulated intensity gradient and texture gradient. Image segmentation is an integral part of image analysis. In image processing different processes are done on images to enhance it at different parameters. These processes ...

Shape Gradient for Image and Video Segmentation

In this chapter, we propose to concentrate on the research of an optimal domain with regards to a global criterion including region and boundary functionals. A local shape minimizer is obtained through the evolution of a deformable domain in the direction of the shape gradient. Shape derivation tools, coming from shape optimization theory, allow us to easily differentiate region and boundary fu...

Normalized Gradient Vector Diffusion and Image Segmentation