'Penerbit Universiti Kebangsaan Malaysia (UKM Press)'
Abstract
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