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

S. Anuradha | Zendy; Ch. Satyanarayana | Zendy; Y. Srinivas | Zendy

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

Author(s) -

S. Anuradha,

Ch. Satyanarayana,

Y. Srinivas

Publication year - 2018

Publication title -

international journal of image graphics and signal processing

Language(s) - English

Resource type - Journals

eISSN - 2074-9082

pISSN - 2074-9074

DOI - 10.5815/ijigsp.2018.08.06

Subject(s) - computer science , pattern recognition (psychology) , artificial intelligence , bivariate analysis , multiple sclerosis , gaussian , identification (biology) , spinal cord , beta (programming language) , artificial neural network , mixture model , brain disease , beta distribution , disease , machine learning , medicine , mathematics , pathology , statistics , physics , quantum mechanics , programming language , botany , psychiatry , biology

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.

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