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Cell nuclei segmentation for histopathological image analysis

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.