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

作者：Yurtsever Ulas^*; Evirgen Hayrettin; Avunduk Mustafa Cihat

来源：Tehnicki Vjesnik, 2018, 25(2): 382-389.

DOI：10.17559/TV-20160923122225

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

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.

• 出版日期2018-4