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


YURTSEVER U., Evirgen H. , AVUNDUK M. C.

TEHNICKI VJESNIK-TECHNICAL GAZETTE, vol.25, no.2, pp.382-389, 2018 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 2
  • Publication Date: 2018
  • Doi Number: 10.17559/tv-20160923122225
  • Title of Journal : TEHNICKI VJESNIK-TECHNICAL GAZETTE
  • Page Numbers: pp.382-389

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.