Glandular cavity segmentation based on local correntropy-based K-means (LCK) clustering and morphological operations

Yingjun Ma; Muhammad Umair Hassan; Dongmei Niu; Liping Wang

doi:10.1117/12.2502002

26 July 2018 Glandular cavity segmentation based on local correntropy-based K-means (LCK) clustering and morphological operations

Yingjun Ma, Muhammad Umair Hassan, Dongmei Niu, Liping Wang

Author Affiliations +

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108280H (2018) https://doi.org/10.1117/12.2502002
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

Abstract

One of the ways to diagnose cancer is to obtain images of the cells under the microscope through biopsies. Because the images of the stained cells are very complicated, there is a great deal of interference with the doctor's observations. To address this issue, we propose a new method for segmenting glandular cavity from gastric cancer cell images. Our method combines local correntropy-based K-means (LCK) clustering method and morphological operations to divide the image into complete glandular cavity and remove all extra-cavity interference areas. Our method does not require human interaction. The acquired image boundary features and internal information are complete, allowing doctors to diagnose cancer more quickly and efficiently.

Citation Download Citation

Yingjun Ma, Muhammad Umair Hassan, Dongmei Niu, and Liping Wang "Glandular cavity segmentation based on local correntropy-based K-means (LCK) clustering and morphological operations", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280H (26 July 2018); https://doi.org/10.1117/12.2502002

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