{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:20:59Z","timestamp":1773098459307,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"TM R&D","award":["RDTC\/231094"],"award-info":[{"award-number":["RDTC\/231094"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Image segmentation is one of the important applications of deep learning models, such as U-Net and Mask R-CNN, in medical imaging. The image segmentation process enables automated extraction of important information within images, including spine X-rays, saving medical practitioners hours of work. However, for X-ray images, low contrast and noise may affect the quality of the images and consequently reduce the effectiveness of the deep learning models in providing a robust segmentation. Image enhancement prior to feeding the images to segmentation models can help to overcome the issues caused by the low-quality images. This paper aims to evaluate the effects of three image enhancement methods, namely, the contrast-limited adaptive histogram equalization (CLAHE), histogram equalization (HE), and anisotropic diffusion (AD), for improving image segmentation performance of Mask R-CNN, non-transfer learning Mask R-CNN, and U-Net. The findings show image enhancement methods provide significant improvement to the U-Net, and, interestingly, no noticeable improvement of performance on Mask R-CNN is observed. The application of HE for transfer learning Mask R-CNN achieved the highest Dice score of 0.942 \u00b1 0.001 for binary segmentation. The randomly initialized Mask R-CNN obtains the highest DSC of 0.941 \u00b1 0.002 on the same task. On the other hand, for U-Net, despite the presence of statistically significant change by applying image enhancement methods, the model achieves a maximum Dice score of 0.916 \u00b1 0.003, lower than Mask R-CNN with and without transfer learning. A study on image enhancement methods and recent deep learning algorithms is necessary to better understand the effect of image enhancement techniques using deep learning.<\/jats:p>","DOI":"10.3390\/a18120796","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T11:00:26Z","timestamp":1765882826000},"page":"796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Impact of Image Enhancement Using Contrast-Limited Adaptive Histogram Equalization (CLAHE), Anisotropic Diffusion, and Histogram Equalization on Spine X-Ray Segmentation with U-Net, Mask R-CNN, and Transfer Learning"],"prefix":"10.3390","volume":"18","author":[{"given":"Muhammad Shahrul Zaim","family":"Ahmad","sequence":"first","affiliation":[{"name":"Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"Nor Azlina Ab.","family":"Aziz","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia"},{"name":"Centre for Advanced Analytics, COE for Artificial Intelligence, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"Heng Siong","family":"Lim","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"Anith Khairunnisa","family":"Ghazali","sequence":"additional","affiliation":[{"name":"Centre for Advanced Analytics, COE for Artificial Intelligence, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"\u2018Afif Abdul","family":"Latiff","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.mri.2019.02.013","article-title":"Differentiation of Spinal Metastases Originated from Lung and Other Cancers Using Radiomics and Deep Learning Based on DCE-MRI","volume":"64","author":"Lang","year":"2019","journal-title":"Magn. 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