Low Complexity Random Noise Denoising Method for Medical Image Analysis
G. Vimala Kumari · International Journal for Research in Applied Science and Engineering Technology · 2020
The medical images are often corrupted by noise during acquisition and transmission due to patient movement, inaccurate instrumental setup and surrounding noise. The noise usually reduces the visual quality of the medical images that complicates diagnosis and treatment. Hence, the need for an efficient denoising method has led to extensive research and development of various innovative methods to remove the random valued impulse noise. For this, a method which detects and filters random valued impulse noise in medical images is employed. The method proposed in this paper uses a decision tree based impulse detector and an edge preserving filter to reconstruct noise free images. The method requires less storage space due to its lower complexity and is more efficient than the existing techniques. Different Magnetic Resonance Imaging (MRI) images are tested by using the algorithm and it gave better Peak Signal to Noise Ratio (PSNR) than the other lower complexity techniques.