Optimal Net Surface Problems with Applications
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Efficient algorithms for segmenting globally optimal and smooth multi-surfaces
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
A new implicit method for surface segmentation by minimal paths: applications in 3D medical images
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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This paper considers the problem of segmenting an accurate and smooth surface from 3D volumetric images. Despite extensive studies in the past, the segmentation problem remains challenging in medical imaging, and becomes even harder in highly noisy and edge-weak images. In this paper we present a highly efficient graph-theoretical approach for segmenting a surface from 3D OCT images. Our approach adopts an objective function that combines the weight and the smoothness of the surface so that the resulting segmentation achieves global optimality and smoothness simultaneously. Based on a volumetric graph representation of the 3D images that incorporates curvature information, our approach first generates a set of 2D local optimal segmentations, and then iteratively improves the solution by fast local computation at regions where significant improvement can be achieved. It can be shown that our approach monotonically improves the quality of solution and converges rather quickly to the global optimal solution. To evaluate the convergence and performance of our method, we test it on both artificial data sets and a set of 14 3D OCT images. Our experiments suggest that the proposed method yields optimal (or almost optimal) solutions in 3 to 5 iterations. Comparing to the existing approaches, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images. Our approach can be easily generalized to multi-surface detection.