A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer
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 Clusterin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2018-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/293192 |
| _version_ | 1797207674947895296 |
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| author | Ulaş Yurtsever Hayrettin Evirgen Mustafa Cihat Avunduk |
| author_facet | Ulaş Yurtsever Hayrettin Evirgen Mustafa Cihat Avunduk |
| author_sort | Ulaş Yurtsever |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T09:26:40Z |
| format | Article |
| id | doaj.art-58e2db34981d45eb8a8292b81cf856a1 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:26:40Z |
| publishDate | 2018-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-58e2db34981d45eb8a8292b81cf856a12024-04-15T14:43:31ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392018-01-0125238238910.17559/TV-20160923122225A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon CancerUlaş Yurtsever0Hayrettin Evirgen1Mustafa Cihat Avunduk2Sakarya University, Institute of Natural Sciences, Computer and Information Engineering, 54187, Sakarya, Turkeyİstanbul University, Faculty of Open and Distance Education, İstanbul, TurkeyNecmettin Erbakan University, Faculty of Meram Madical, Department of Pathology, Konya, TurkeyIn 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.https://hrcak.srce.hr/file/293192cancer detectionclustering algorithmshistopathological image analysisimage segmentationk-means |
| spellingShingle | Ulaş Yurtsever Hayrettin Evirgen Mustafa Cihat Avunduk A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer Tehnički Vjesnik cancer detection clustering algorithms histopathological image analysis image segmentation k-means |
| title | A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer |
| title_full | A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer |
| title_fullStr | A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer |
| title_full_unstemmed | A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer |
| title_short | A New Augmented K-Means Algorithm for Seed Segmentation in Microscopic Images of the Colon Cancer |
| title_sort | new augmented k means algorithm for seed segmentation in microscopic images of the colon cancer |
| topic | cancer detection clustering algorithms histopathological image analysis image segmentation k-means |
| url | https://hrcak.srce.hr/file/293192 |
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