Patch-Cuts: A Graph-Based Image Segmentation Method Using Patch Features and Spatial Relations

In this paper, we present a graph-based image segmentation method (patch-cuts) that incorporates features and spatial relations obtained from image patches. In the first step, patch-cuts extracts a set of patches that can assume arbitrary shape and size. Patches are determined by a combination of intensity quantization and morphological operations and render the proposed method robust against noise. Upon patch extraction, a set of intensity, texture and shape features are computed for each patch. These features are integrated and minimized simultaneously in a tunable energy function. Patch-cuts explores the benefit of information theory-based measures such as the Kullback-Leibler and the Jensen-Shannon divergence in its energy terms. In our experiments, we applied patchcuts to general images as well as to non-contrast Computed Tomography heart scans.

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Gerd Brunner, Deepak Roy Chittajallu, Uday Kurkure, Ioannis A. Kakadiaris
• Keywords: BMVC 2010, Computer Vision, Graph-based Image Segmentation, Non-contrast Computed Tomography, Tunable Energy Function