Active Contour Models for Manifold Valued Image Segmentation

TL;DR

This paper extends active contour models to segment manifold-valued images, enabling analysis of complex data types like DT-MRI and vector fields, and demonstrates its effectiveness on various image segmentation tasks.

Contribution

The paper introduces a generalized active contour framework for manifold-valued images, broadening the applicability of segmentation methods to complex data modalities.

Findings

Effective segmentation of manifold-valued images demonstrated

Successful texture segmentation via manifold image creation

Compatible with traditional gray-scale image segmentation

Abstract

Image segmentation is the process of partitioning a image into different regions or groups based on some characteristics like color, texture, motion or shape etc. Active contours is a popular variational method for object segmentation in images, in which the user initializes a contour which evolves in order to optimize an objective function designed such that the desired object boundary is the optimal solution. Recently, imaging modalities that produce Manifold valued images have come up, for example, DT-MRI images, vector fields. The traditional active contour model does not work on such images. In this paper, we generalize the active contour model to work on Manifold valued images. As expected, our algorithm detects regions with similar Manifold values in the image. Our algorithm also produces expected results on usual gray-scale images, since these are nothing but trivial examples of Manifold valued images. As another application of our general active contour model, we perform texture segmentation on gray-scale images by first creating an appropriate Manifold valued image. We demonstrate segmentation results for manifold valued images and texture images.