A Novel Approach to Gene Selection of Leukemia Dataset Using Different Clustering Methods

Prasath, P.; Perumal, K.; Thangavel, K.; Manavalan, R.

doi:10.1007/978-81-322-1680-3_7

Cited by 1 publication

(1 citation statement)

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“…Such algorithms are divided into 5 main groups in general [6][7][8][9], namely, region-based segmentation algorithms [10][11][12][13][14], edge-based segmentation algorithms [15,16,17], thresholding-based segmentation algorithms [18,19], computational or clustering-based segmentation algorithms [20][21][22][23][24][25][26] and graph-based segmentation algorithms [27,28,29]. This study proposes an improved version of K-Means Clustering algorithm which is a clustering-based segmentation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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