Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine


  • B Hemalatha Bharath Institute of Higher Education and Research
  • B Karthik Bharath Institute of Higher Education and Research
  • C V Krishna Reddy Nalla Narasimha Reddy Education Society's Group of Institutions




Signal to Noise Ratio, Speckle, MRI Images, Classification, Mean Filter, Multi SVM


INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine.

OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing.

METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise.

RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images.

CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier.  The entire system developed using python programming.


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How to Cite

Hemalatha B, Karthik B, Krishna Reddy CV. Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Feb. 8 [cited 2024 Feb. 22];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5076