Leukemia Nucleus Image Segmentation Using Covering-Based Rough K-Means Clustering Algorithm

13 Pages Posted: 7 Mar 2018

See all articles by G Jothi

G Jothi

Sona College of Technology

H Hannah Inbarani

Periyar University

Date Written: November 15, 2017

Abstract

Image Segmentation plays an important role in leukemia diagnosis. Blast and non-blast cells are identified based on the shape and size of the leukemia nucleus. Accurate and efficient segmentation technique is essential for earlier prediction of leukemia. In recent years, rough set based clustering has become a user-friendly technique employed to segment the cancerous cells in medical image processing. In this paper, Covering-based Rough K-Means (CRKM) clustering algorithm is proposed to segment the image of leukemia nucleus. In this method, covering-based approximation is used instead of rough approximation. The robustness of the proposed method is compared with the existing clustering-based segmentation techniques, such as, K-means clustering, Fuzzy C-means (FCM) clustering and Rough K-means (RKM) clustering. The result reveals that the proposed CRKM clustering algorithm is robust in segmenting the image of the nucleus.

Keywords: Clustering, Covering Rough Set, Leukemia, Segmentation

Suggested Citation

Jothi, G and Hannah Inbarani, H, Leukemia Nucleus Image Segmentation Using Covering-Based Rough K-Means Clustering Algorithm (November 15, 2017). Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017 – Dec 15th - 16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India, Available at SSRN: https://ssrn.com/abstract=3131633 or http://dx.doi.org/10.2139/ssrn.3131633

G Jothi (Contact Author)

Sona College of Technology ( email )

Junction Main Road
Suramangalam
Salem, Tamil Nadu 636005
India

H Hannah Inbarani

Periyar University ( email )

Periyar Palkalai Nagar
Salem, Tamil Nadu 636011
India

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