Prominent region of interest contrast enhancement for knee MR images: data from the OAI
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 visua...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2020
|
| Online Access: | http://journalarticle.ukm.my/17136/ http://journalarticle.ukm.my/17136/1/15.pdf |
| _version_ | 1848814236428402688 |
|---|---|
| author | Sia, Joyce Sin Yin Tan, Tian Swee Azli Yahya, Tiong, Matthias Foh Thye Ling, Kelvin Chia Hiik Leong, Kah Meng Tan, Jia Hou Sameen Ahmed Malik, Hum, Yan Chai Khairil Amir Sayuti, Ahmad Tarmizi Musa, |
| author_facet | Sia, Joyce Sin Yin Tan, Tian Swee Azli Yahya, Tiong, Matthias Foh Thye Ling, Kelvin Chia Hiik Leong, Kah Meng Tan, Jia Hou Sameen Ahmed Malik, Hum, Yan Chai Khairil Amir Sayuti, Ahmad Tarmizi Musa, |
| author_sort | Sia, Joyce Sin Yin |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T00:30:53Z |
| format | Article |
| id | oai:generic.eprints.org:17136 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:30:53Z |
| publishDate | 2020 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:171362021-07-21T06:51:23Z http://journalarticle.ukm.my/17136/ Prominent region of interest contrast enhancement for knee MR images: data from the OAI Sia, Joyce Sin Yin Tan, Tian Swee Azli Yahya, Tiong, Matthias Foh Thye Ling, Kelvin Chia Hiik Leong, Kah Meng Tan, Jia Hou Sameen Ahmed Malik, Hum, Yan Chai Khairil Amir Sayuti, Ahmad Tarmizi Musa, 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. Penerbit Universiti Kebangsaan Malaysia 2020 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17136/1/15.pdf Sia, Joyce Sin Yin and Tan, Tian Swee and Azli Yahya, and Tiong, Matthias Foh Thye and Ling, Kelvin Chia Hiik and Leong, Kah Meng and Tan, Jia Hou and Sameen Ahmed Malik, and Hum, Yan Chai and Khairil Amir Sayuti, and Ahmad Tarmizi Musa, (2020) Prominent region of interest contrast enhancement for knee MR images: data from the OAI. Jurnal Kejuruteraan, 32 (3). pp. 501-511. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-323-2020/ |
| spellingShingle | Sia, Joyce Sin Yin Tan, Tian Swee Azli Yahya, Tiong, Matthias Foh Thye Ling, Kelvin Chia Hiik Leong, Kah Meng Tan, Jia Hou Sameen Ahmed Malik, Hum, Yan Chai Khairil Amir Sayuti, Ahmad Tarmizi Musa, Prominent region of interest contrast enhancement for knee MR images: data from the OAI |
| title | Prominent region of interest contrast enhancement for knee MR images: data from the OAI |
| title_full | Prominent region of interest contrast enhancement for knee MR images: data from the OAI |
| title_fullStr | Prominent region of interest contrast enhancement for knee MR images: data from the OAI |
| title_full_unstemmed | Prominent region of interest contrast enhancement for knee MR images: data from the OAI |
| title_short | Prominent region of interest contrast enhancement for knee MR images: data from the OAI |
| title_sort | prominent region of interest contrast enhancement for knee mr images: data from the oai |
| url | http://journalarticle.ukm.my/17136/ http://journalarticle.ukm.my/17136/ http://journalarticle.ukm.my/17136/1/15.pdf |