MRF reconstruction of retinal images for the optic disc segmentation
HIS'12 Proceedings of the First international conference on Health Information Science
Computer Vision and Image Understanding
Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior
Journal of Mathematical Imaging and Vision
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Segmentation of medical images is an important step in many clinical and diagnostic imaging applications. Medical images present many challenges for automated segmentation including poor contrast at tissue boundaries. Traditional segmentation methods based solely on information from the image do not work well in such cases. Statistical shape information for objects in medical images are easy to obtain. In this paper, we propose a graph cuts-based segmentation method for medical images that incorporates statistical shape priors to increase robustness. Our proposed method is able to deal with complex shapes and shape variations while taking advantage of the globally efficient optimization by graph cuts. We demonstrate the effectiveness of our method on kidney images without strong boundaries.