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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 icon 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

Puni tekst: engleski, pdf (4 MB) str. 382-389 preuzimanja: 652* citiraj
APA 6th Edition
Yurtsever, U., Evirgen, H. i Avunduk, M.C. (2018). A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer. Tehnički vjesnik, 25 (2), 382-389. https://doi.org/10.17559/TV-20160923122225
MLA 8th Edition
Yurtsever, Ulaş, et al. "A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer." Tehnički vjesnik, vol. 25, br. 2, 2018, str. 382-389. https://doi.org/10.17559/TV-20160923122225. Citirano 15.05.2021.
Chicago 17th Edition
Yurtsever, Ulaş, Hayrettin Evirgen i Mustafa Cihat Avunduk. "A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer." Tehnički vjesnik 25, br. 2 (2018): 382-389. https://doi.org/10.17559/TV-20160923122225
Harvard
Yurtsever, U., Evirgen, H., i Avunduk, M.C. (2018). 'A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer', Tehnički vjesnik, 25(2), str. 382-389. https://doi.org/10.17559/TV-20160923122225
Vancouver
Yurtsever U, Evirgen H, Avunduk MC. A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer. Tehnički vjesnik [Internet]. 2018 [pristupljeno 15.05.2021.];25(2):382-389. https://doi.org/10.17559/TV-20160923122225
IEEE
U. Yurtsever, H. Evirgen i M.C. Avunduk, "A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer", Tehnički vjesnik, vol.25, br. 2, str. 382-389, 2018. [Online]. https://doi.org/10.17559/TV-20160923122225

Sažetak
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.

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

Hrčak ID: 199134

URI
https://hrcak.srce.hr/199134

Posjeta: 935 *