A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images.
Muhammad Waqar MirzaAsif SiddiqIshtiaq Rasool KhanPublished in: Signal, image and video processing (2022)
Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.
Keyphrases
- deep learning
- computed tomography
- sars cov
- dual energy
- contrast enhanced
- early stage
- coronavirus disease
- high resolution
- image quality
- healthcare
- artificial intelligence
- convolutional neural network
- machine learning
- positron emission tomography
- magnetic resonance
- squamous cell carcinoma
- mass spectrometry
- respiratory syndrome coronavirus
- lymph node
- photodynamic therapy
- sentinel lymph node
- rectal cancer