Open Access

ARTICLE

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

by S. Sreedhar Kumar¹, Syed Thouheed Ahmed^2,*, Qin Xin³, S. Sandeep⁴, M. Madheswaran⁵, Syed Muzamil Basha²

1 Dr. T. Thimmaiah Institute of Technology, VTU, KGF, Karnataka, India
2 School of Computing & Information Technology, REVA University, Bengaluru, India
3 Faculty of Science and Technology, University of the Faroe Islands, Faroe Islands, Denmark
4 K S School of Engineering, Bengaluru, India
5 Muthayammal Engineering College, Rasipuram, Tamil Nadu, India

* Corresponding Author: Syed Thouheed Ahmed. Email:

(This article belongs to the Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 72(1), 281-299. https://doi.org/10.32604/cmc.2022.023693

Received 17 September 2021; Accepted 10 December 2021; Issue published 24 February 2022

Abstract

This paper presents, a new approach of Medical Image Pixels Clustering (MIPC), aims to trace the dissimilar patterns over the Magnetic Resonance (MR) image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment, pattern predication and deeper investigation. The proposed MIPC consists of two stages: clustering and validation. In the clustering stage, the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering (iLIAC), Dynamic Automatic Agglomerative Clustering (DAAC) and Optimum N-Means (ONM). In the second stage, the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure (SICM). Experimental results show that the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.

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Keywords

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