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Proceedings Paper

Cell nuclei segmentation for histopathological image analysis
Author(s): Hui Kong; Kamel Belkacem-Boussaid; Metin Gurcan
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Paper Abstract

In this paper, we propose a supervised method for segmenting cell nuclei from background and extra-cellular regions in pathological images. To this end, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC color space can achieve the most discrimination in terms of classification (segmentation) performance. To speed up the texture feature extraction process, we develop an efficient LFT extraction algorithm based on image shifting and image integral. For evaluation, our method is compared with the state-of-the-art segmentation algorithms (Graph-cut, Mean-shift, etc.). Empirical results show that our segmentation method achieves better performance than these popular methods.

Paper Details

Date Published: 14 March 2011
PDF: 14 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622R (14 March 2011); doi: 10.1117/12.878018
Show Author Affiliations
Hui Kong, Ohio State Univ. (United States)
Kamel Belkacem-Boussaid, Ohio State Univ. (United States)
Metin Gurcan, Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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