2010/06/21 by Noha El-Zehiry, El-Zehiry, Noha, Leo Grady +1
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Digital Image Processing Techniques #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1006.4175
15 pages , 6 figures
arxiv created 2010/06/21 · openalex publication_date 2010/06/21 · arxiv updated 2010/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from descent methods which could be trapped in a local minimum and therefore required a good initialization. Recently, combinatorial optimization techniques have been applied to the optimization of curvature which provide a solution that achieves nearly a global optimum. However, when applied to image segmentation these methods required a meaningful data term. Unfortunately, for many images, particularly medical images, it is difficult to find a meaningful data term. Therefore, we propose to remove the data term completely and instead weight the curvature locally, while still achieving a global optimum.