An Approach for Analyzing Noisy Multiple Sclerosis Images Using Truncated Beta Gaussian Mixture Model

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S. Anuradha 1,* Ch. Satyanarayana 2 Y.Srinivas 3

1. Department of CSE, GITAM (Deemed to be University), Visakhapatnam, A.P., India

2. Department of CSE, University College of Engineering, JNTUK, Kakinada, A.P., India.

3. Department of IT, GITAM (Deemed to be University), Visakhapatnam, A.P., India

* Corresponding author.


Received: 1 Dec. 2017 / Revised: 18 Apr. 2018 / Accepted: 13 Jun. 2018 / Published: 8 Aug. 2018

Index Terms

Neural Network, Clustering, Classification and Mixture models


Sclerosis is a disease that triggers mainly due to damage of nerve cells in the brain and spinal cord. Various impairments are observed with this disease. Analyzing this type of images is needed for the medical research field for early stage identification. So, the present paper uses Bivariate Gaussian Mixture distribution for analyzing the noisy sclerosis images. For this, the present paper uses neural network for classification. The proposed method is evaluated with various images of brain web repository and the results show the efficiency of the proposed method. 

Cite This Paper

S. Anuradha, Ch. Satyanarayana, Y. Srinivas, " An Approach for Analyzing Noisy Multiple Sclerosis Images Using Truncated Beta Gaussian Mixture Model ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.8, pp. 54-60, 2018. DOI: 10.5815/ijigsp.2018.08.06


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