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Proceedings Paper

Adaptive local thresholding for robust nucleus segmentation utilizing shape priors
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Paper Abstract

This paper describes a novel local thresholding method for foreground detection. First, a Canny edge detection method is used for initial edge detection. Then, tensor voting is applied on the initial edge pixels, using a nonsymmetric tensor field tailored to encode prior information about nucleus size, shape, and intensity spatial distribution. Tensor analysis is then performed to generate the saliency image and, based on that, the refined edge. Next, the image domain is divided into blocks. In each block, at least one foreground and one background pixel are sampled for each refined edge pixel. The saliency weighted foreground histogram and background histogram are then created. These two histograms are used to calculate a threshold by minimizing the background and foreground pixel classification error. The block-wise thresholds are then used to generate the threshold for each pixel via interpolation. Finally, the foreground is obtained by comparing the original image with the threshold image. The effective use of prior information, combined with robust techniques, results in far more reliable foreground detection, which leads to robust nucleus segmentation.

Paper Details

Date Published: 23 March 2016
PDF: 6 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910C (23 March 2016); doi: 10.1117/12.2216334
Show Author Affiliations
Xiuzhong Wang, Ventana Medical Systems, Inc. (United States)
Chukka Srinivas, Ventana Medical Systems, Inc. (United States)

Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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