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A Novel Medical Image de-Noising Algorithm for Efficient Diagnosis in Smart Health Environment

EasyChair Preprint no. 7571

5 pagesDate: March 17, 2022


Smart healthcare is defined by the technology that leads to better diagnostic tools, better treatment for patients, and devices that improve the quality of life for anyone and everyone. Medical images have significant to facilitate that smart health environment. However, the medical images have frequently gotten noisy in the acquisition process, which engages many different physical mechanisms. Most of the de-noising algorithms conceive the additive white Gaussian noise (AWGN). However, among the popular medical image modalities, several are degraded by some type of non-Gaussian noise such as Poisson noise. Poisson noise is mainly associated with many imaging modalities like single-photon emission computerized tomography (SPECT), (positron emission tomography) PET, and fluorescent confocal microscopy imaging. Because of the signal-dependent nature of Poisson noise, the various de-noising filters proposed in the literature, including the Non-Local Mean (NLM) filter. In literature, NLM is mostly applied for Gaussian noise extraction and very rarely used for Poisson noise removal. In this work, notable efforts are put to modified NLM filter, and high order NL-Means Methods are proposed. These novel high order algorithms de-noise images by prominent the signals and noise because it takes the high order odd moment of the medical image. The visual quality of the de-noised medical image (PET) and correlation graph determines that the proposed algorithms outperform the conventional denoising filter. The findings of this study will significantly contribute towards the development of a more accurate and robust image analysis model, which is the need of today's modern age of digitization.

Keyphrases: De-noise Poisson noise-contaminated images, Improve medical images visual quality, Medical Images De-noising, Poisson Noise in medical images, Poisson Noise Removal, Smart Health

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Khalid Mahboob and Shahzad Khursheed and Syed Muslim Jameel and Vali Uddin and Saurabh Shukla and Jitander Kumar Pabani},
  title = {A Novel Medical Image de-Noising Algorithm for Efficient Diagnosis in Smart Health Environment},
  howpublished = {EasyChair Preprint no. 7571},

  year = {EasyChair, 2022}}
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