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Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

S. Sreedhar Kumar1, Syed Thouheed Ahmed2,*, 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 this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 72(1), 281-299.


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.


Cite This Article

S. Sreedhar Kumar, S. Thouheed Ahmed, Q. Xin, S. Sandeep, M. Madheswaran et al., "Unstructured oncological image cluster identification using improved unsupervised clustering techniques," Computers, Materials & Continua, vol. 72, no.1, pp. 281–299, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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