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, cilt.25, sa.2, ss.382-389, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.17559/tv-20160923122225
  • Dergi Adı: TEHNICKI VJESNIK-TECHNICAL GAZETTE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.382-389
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.