MRI Classification of Parkinson’s Disease Using SVM and Texture Features

  • S. Pazhanirajan
  • P. Dhanalakshmi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)


A novel method for automatic classification of magnetic resonance image (MRI) under categories of normal and Parkinson’s disease (PD) is then classified according to the severity of the medical specialty drawbacks. In recent years, with the advancement in all fields, human suffers from numerous specialty disorders like brain disorder, epilepsy, Alzheimer, Parkinson, etc. Parkinson’s involves the malfunction and death of significant nerve cells within the brain, known as neurons. As metal progresses, the quantity of Dopastat made within the brain decreases, defeat someone, and make them unable to manage movements commonly. In the planned system, T2 (spin-spin relaxation time)—weighted MR images are obtained from the potential PD subjects. For categorizing the MRI knowledge, bar graph options and gray level co-occurrence matrix (GLCM) options are extracted. The options obtained are given as input to the SVM classifier that classifies the information into traditional or PD classes. The system shows a satisfactory performance of quite 87 %.


Electroencephalogram (EEG) Parkinson’s disease (PD) Gray-level co-occurrence matrix (GLCM) Support vector machine (SVM) 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAnnamalai UniversityChidambaramIndia

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