Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Unsupervised segmentation based on robust estimation and color active contour models
IEEE Transactions on Information Technology in Biomedicine
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Accurate segmentation of tissue microarrays is a challenging topic because of some of the similarities exhibited by normal tissue and tumor regions. Processing speed is another consideration when dealing with imaged tissue microarrays as each microscopic slide may contain hundreds of digitized tissue discs. In this paper, a fast and accurate image segmentation algorithm is presented. Both a whole disc delineation algorithm and a learning based tumor region segmentation approach which utilizes multiple scale texton histograms are introduced. The algorithm is completely automatic and computationally efficient. The mean pixel-wise segmentation accuracy is about 90%. It requires about 1 second for whole disc (1024×1024 pixels) segmentation and less than 5 seconds for segmenting tumor regions. In order to enable remote access to the algorithm and collaborative studies, an analytical service is implemented using the caGrid infrastructure. This service wraps the algorithm and provides interfaces for remote clients to submit images for analysis and retrieve analysis results.