Short Notes on Unsupervised Learning Method with Clustering Approach forTumor Identification and Tissue Segmentation in Magnetic Resonance Brain Images| Abstract

Journal of Clinical & Experimental Neuroimmunology
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  • Research Article   
  • J Clin Exp Neuroimmunol 1:101,

Short Notes on Unsupervised Learning Method with Clustering Approach forTumor Identification and Tissue Segmentation in Magnetic Resonance Brain Images

Vishnuvarthanan Govindaraj*, Anitha Vishnuvarthanan, Arunprasath Thiagarajan, Kannan M and Pallikonda Rajasekaran Murugan
Kalasalingam University, Virudhunagar, Tamilnadu, India
*Corresponding Author : Vishnuvarthanan Govindaraj, Kalasalingam University, Virudhunagar, Tamilnadu, India, Tel: +91 9360654171, Email: [email protected]

Received Date: Dec 05, 2015 / Accepted Date: Jan 04, 2016 / Published Date: Jan 07, 2016


Malignant and benign types of tumor, infiltrated in human brain are diagnosed with the help of an MRI scanner. Using the slice images obtained using an MRI scanner; certain image processing techniques are implemented to have a clear anatomy of brain tissues. One such image processing technique is hybrid Self Organizing Map (SOM) with Fuzzy K Means (FKM) algorithm, which offers a possible identification of tumor region penetration in the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), Sensitivity, Specificity, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Computational time and memory requirement for processing the Magnetic Resonance (MR) brain images. Automatic detection of tumor region in MR (Magnetic Resonance) brain images has a good impact in helping the radio surgeons to identify the exact topographical location of tumor region. In this paper, the proposed hybrid SOM – FKM algorithm supports the radio surgeon by providing tissue segmentation and an automated tumor identification.

Keywords: SOM (Self Organizing Map); FKM (Fuzzy K – Means algorithm); Tumor segmentation; Peak Signal to Noise Ratio (PSNR); Jaccard Index; Dice Overlap Index (DOI); Sensitivity; Specificity; Mean Square Error (MSE); Brain image segmentation and Computational time

Citation: Govindaraj V, Vishnuvarthanan A, Thiagarajan A, Kannan M, Murugan PR (2016) Short Notes on Unsupervised Learning Method with Clustering Approach for Tumor Identification and Tissue Segmentation in Magnetic Resonance Brain Images. J Clin Exp Neuroimmunol 1:101.

Copyright: ©2016 Govindaraj V, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Review summary

  1. Davian Brooks
    Posted on Sep 26 2016 at 12:22 pm
    The manuscript is written in a concise and factual manner. The article outlines algorithm for better analysis of MRI images and providing faster and accurate results to the patients.