Prominent Region of Interest Contrast Enhancement for Knee MR Images: Data from the OAI
Authors:   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Sin Yin Joyce Sia , Tian Swee Tan ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Bin Yahya Azli ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Foh Thye Matthias Tiong ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Chia Hiik Kelvin Ling ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Kah Meng Leong ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Jia Hou Tan ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Ahmed Malik Sameen ,   Biomedical Engineering Programme, School of Biomedical Engineering and Health Sciences, Faculty of Engineering, Universiti Teknologi Malaysia, Malaysia, Yan Chai Hum ,   Department of Mechatronics and Biomedical Engineering,  Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Malaysia, Bin Sayuti Khairil Amir ,   Department of Radiology, Universiti Sains Malaysia, Malaysia, Tarmizi Musa Ahmad ,   Department of Radiology, Universiti Sains Malaysia, Malaysia
Published: February 2023

Osteoarthritis is the most commonly seen arthritis, where there are 30.8 million adults affected in 2015. Magnetic resonance imaging (MRI) plays a key role to provide direct visualization and quantitative measurement on knee cartilage to monitor the osteoarthritis progression. However, the visual quality of MRI data can be influenced by poor background luminance, complex human knee anatomy, and indistinctive tissue contrast. Typical histogram equalisation methods are proven to be irrelevant in processing the biomedical images due to their steep cumulative density function (CDF) mapping curve which could result in severe washout and distortion on subject details. In this paper, the prominent region of interest contrast enhancement method (PROICE) is proposed to separate the original histogram of a 16-bit biomedical image into two Gaussians that cover dark pixels region and bright pixels region respectively. After obtaining the mean of the brighter region, where our ROI – knee cartilage falls, the mean becomes a break point to process two Bezier transform curves separately. The Bezier curves are then combined to replace the typical CDF curve to equalize the original histogram. The enhanced image preserves knee feature as well as region of interest (ROI) mean brightness. The image enhancement performance tests show that PROICE has achieved the highest peak signal-to-noise ratio (PSNR=24.747±1.315dB), lowest absolute mean brightness error (AMBE=0.020±0.007) and notably structural similarity index (SSIM=0.935±0.019). In other words, PROICE has considerably outperformed the other approaches in terms of its noise reduction, perceived image quality, its precision and has shown great potential to visually assist physicians in their diagnosis and decision-making process.