Determining the number of clusters for nuclei segmentation in breast cancer image

Chastine Fatichah; Dini Adni Navastara; Nanik Suciati; Lubna Nuraini

doi:10.1117/12.2266980

8 February 2017 Determining the number of clusters for nuclei segmentation in breast cancer image

Chastine Fatichah, Dini Adni Navastara, Nanik Suciati, Lubna Nuraini

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102252E (2017) https://doi.org/10.1117/12.2266980
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

Clustering is commonly technique for image segmentation, however determining an appropriate number of clusters is still challenging. Due to nuclei variation of size and shape in breast cancer image, an automatic determining number of clusters for segmenting the nuclei breast cancer is proposed. The phase of nuclei segmentation in breast cancer image are nuclei detection, touched nuclei detection, and touched nuclei separation. We use the Gram-Schmidt for nuclei cell detection, the geometry feature for touched nuclei detection, and combining of watershed and spatial k-Means clustering for separating the touched nuclei in breast cancer image. The spatial k-Means clustering is employed for separating the touched nuclei, however automatically determine the number of clusters is difficult due to the variation of size and shape of single cell breast cancer. To overcome this problem, first we apply watershed algorithm to separate the touched nuclei and then we calculate the distance among centroids in order to solve the over-segmentation. We merge two centroids that have the distance below threshold. And the new of number centroid as input to segment the nuclei cell using spatial k- Means algorithm. Experiment show that, the proposed scheme can improve the accuracy of nuclei cell counting.

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Chastine Fatichah, Dini Adni Navastara, Nanik Suciati, and Lubna Nuraini "Determining the number of clusters for nuclei segmentation in breast cancer image", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102252E (8 February 2017); https://doi.org/10.1117/12.2266980

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KEYWORDS

Image segmentation

Breast cancer

Image processing algorithms and systems

Cancer

Image processing

Expectation maximization algorithms

Floods

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