Original scientific paper

https://doi.org/10.17559/TV-20160923122225

A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer

Ulaş Yurtsever orcid.org/0000-0003-3438-6872 ; Sakarya University, Institute of Natural Sciences, Computer and Information Engineering, 54187, Sakarya, Turkey
Hayrettin Evirgen ; İstanbul University, Faculty of Open and Distance Education, İstanbul, Turkey
Mustafa Cihat Avunduk ; Necmettin Erbakan University, Faculty of Meram Madical, Department of Pathology, Konya, Turkey

Abstract

In this study, we analyze histologic human colon tissue images that we captured with a camera-mounted microscope. We propose the Augmented K-Means Clustering algorithm as a method of segmenting cell nuclei in such colon images. Then we compare the proposed algorithm to the weighted K-Means Clustering algorithm. As a result, we observe that the developed Augmented K-Means Clustering algorithm decreased the needed number of iterations and shortened the duration of the segmentation process. Moreover, the algorithm we propose appears more consistent in comparison to the weighted K-Means Clustering algorithm. We also assess the similarity of the segmented images to the original images, for which we used the Histogram-Based Similarity method. Our assessment indicates that the images segmented by the Augmented K-Means Clustering algorithm are more frequently similar to the original images than the images segmented by the Weighed K-Means Clustering algorithm.

Keywords

cancer detection; clustering algorithms; histopathological image analysis; image segmentation; k-means

Hrčak ID:

199134

URI

https://hrcak.srce.hr/199134

Publication date:

21.4.2018.

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