Parallel marker-based image segmentation with watershed transformation
Journal of Parallel and Distributed Computing
Convex Grouping Combining Boundary and Region Information
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Improved watershed segmentation using water diffusion and local shape priors
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-resolution vessel segmentation using normalized cuts in retinal images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Unsupervised segmentation based on robust estimation and color active contour models
IEEE Transactions on Information Technology in Biomedicine
Computers in Biology and Medicine
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Automatic image analysis of histopathology specimens would help the early detection of blood cancer. The first step for automatic image analysis is segmentation. However, touching cells bring the difficulty for traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably handle touching cells segmentation. Robust estimation and color active contour models are used to delineate the outer boundary. Concave points on the boundary and inner edges are automatically detected. A concave vertex graph is constructed from these points and edges. By minimizing a cost function based on morphological characteristics, we recursively calculate the optimal path in the graph to separate the touching cells. The algorithm is computationally efficient and has been tested on two large clinical dataset which contain 207 images and 3898 images respectively. Our algorithm provides better results than other studies reported in the recent literature.