{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:38:53Z","timestamp":1760060333198,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This study introduces a dual-filter X-ray image enhancement technique designed to elevate the quality of radiographic images of the knee, breast, and wrist, employing the Cream and Bosso algorithms. Our quantitative analysis reveals significant improvements in bone, edge definition, and contrast (p &lt; 0.001). The processing parameters are derived from the relationship between entropy metrics and the filtering parameter d. The results demonstrate contrast enhancements for knee radiographs and for wrist radiographs, while maintaining acceptable noise levels. Comparisons are made with CLAHE techniques, unsharp masking, and deep-learning-based models. This method is a reliable and computationally efficient approach to enhancing clinical diagnosis in resource-limited settings, thereby improving robustness and interpretability.<\/jats:p>","DOI":"10.3390\/jimaging11090291","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T14:18:49Z","timestamp":1756217929000},"page":"291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Filter X-Ray Image Enhancement Using Cream and Bosso Algorithms: Contrast and Entropy Optimization Across Anatomical Regions"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2059-7970","authenticated-orcid":false,"given":"Antonio","family":"Rienzo","sequence":"first","affiliation":[{"name":"Escuela de Ingenier\u00eda Biom\u00e9dica, Universidad de Valpara\u00edso, Valpara\u00edso 2362905, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-5787","authenticated-orcid":false,"given":"Miguel","family":"Bustamante","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias F\u00edsicas, Universidad Andr\u00e9s Bello, Santiago 8370136, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Staub","sequence":"additional","affiliation":[{"name":"Escuela de Medicina, Universidad de Valpara\u00edso, Valpara\u00edso 2580462, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7920-4548","authenticated-orcid":false,"given":"Gast\u00f3n","family":"Lefranc","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda El\u00e9ctrica, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2420000, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"ref_1","unstructured":"Bauer, M. (2025, June 20). Layer Thickness Determination via the Absorption of X-rays. Available online: https:\/\/www.ph.tum.de\/academics\/org\/labs\/fopra\/docs\/userguide-62.en.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1148\/radiol.2021201948","article-title":"Contrast-enhanced Mammography: State of the Art","volume":"299","author":"Jochelson","year":"2021","journal-title":"Radiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/51.464770","article-title":"Wavelets for contrast enhancement of digital mammography","volume":"14","author":"Laine","year":"1995","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_4","unstructured":"Evans, R. (1955). The Atomic Nucleus, McGraw-Hill."},{"key":"ref_5","unstructured":"Lefranc, G., and Bustamante, M. (1999, January 12\u201316). Behaviour of the filter of Bosso in digital images. Proceedings of the ISIE\u201999. Proceedings of the IEEE International Symposium on Industrial Electronics, Bled, Slovenia."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"124","DOI":"10.3182\/20130911-3-BR-3021.00097","article-title":"Behavior of the Bosso Filter and Its Applications to Mammogram","volume":"46","author":"Bustamante","year":"2013","journal-title":"IFAC Proc. Vol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1109\/TLA.2018.8789569","article-title":"Algorithm for processing Mammography: Detection of microcalcifications","volume":"16","author":"Bustamante","year":"2018","journal-title":"IEEE LatAm Trans."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, J., and Kim, J.H. (2022). A Novel Sub-Pixel-Shift-Based High-Resolution X-ray Flat Panel Detector. Coatings, 12.","DOI":"10.3390\/coatings12070921"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, E.J., Kim, D.H., Cha, E.S., and Ro, Y.M. (2014, January 1\u20134). Improvement of subtle microcalcifications detection in DBT slices. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, Spain.","DOI":"10.1109\/BHI.2014.6864368"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Baddar, W.J., Kim, D.H., and Ro, Y.M. (2014, January 1\u20134). Breast tissue removal for enhancing microcalcification cluster detection in mammograms. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, Spain.","DOI":"10.1109\/BHI.2014.6864378"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Iniewski, K., and Cai, L. (2024). Deep Learning for Advanced X-Ray Detection and Imaging Applications, Springer.","DOI":"10.1007\/978-3-031-75653-5"},{"key":"ref_12","unstructured":"Pillai, A.S. (2025). Deep Learning for X-ray Classification: A Comprehensive Review of Methods, Challenges, and Future Prospects. Int. Res. J. Mod. Eng. Technol. Sci., 7."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102125","DOI":"10.1016\/j.media.2021.102125","article-title":"Deep learning for chest X-ray analysis: Survey","volume":"72","author":"Sogancioglu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103615","DOI":"10.1016\/j.micron.2024.103615","article-title":"Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges","volume":"180","author":"Nazir","year":"2024","journal-title":"Micron"},{"key":"ref_15","unstructured":"Xiang, Y., Liu, Z., Garcia-Segura, M., Simon, D., Cao, B., Wu, V., Robinson, K., Wang, Y., Battle, R., and Murray, T.R. (2025). Hyperspectral Image Restoration and Super-reso- lution with Physics-Aware Deep Learning for Biomedical Applications. arXiv."},{"key":"ref_16","unstructured":"Hubbell, J.H., and Seltzer, S.M. (2020, December 08). X-Ray Mass Attenuation Coefficients|NIST, Available online: https:\/\/www.nist.gov\/pml\/x-ray-mass-attenuation-coefficients."},{"key":"ref_17","unstructured":"Firmino, W., and Barreto, G. (2020, January 7\u201311). Contributions to eliminate distortions in medical images. Proceedings of the 9th Brazilian Conference on Dynamics, Control and Applications, Campinas, Brazil."},{"key":"ref_18","unstructured":"Gonzalez, R., and Woods, R. (2008). Digital Image Processing, Pearson. [3rd ed.]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","unstructured":"ASOMEL (2025, August 14). Private Health Clinic with Services and Subspecialties in Traumatology and Orthopedics. Vi\u00f1a del Mar, Chile. Available online: https:\/\/www.asomel.cl\/doctor\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. Graphics Gems IV, Academic Press Professional, Inc.","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF03178082","article-title":"Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms","volume":"11","author":"Pisano","year":"1998","journal-title":"J. Digit. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1109\/83.826787","article-title":"Mathews. Image enhancement via adaptive unsharp masking","volume":"9","author":"Polesel","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0165-1684(98)00038-3","article-title":"A cubic unsharp masking technique for contrast enhancement","volume":"67","author":"Ramponi","year":"1998","journal-title":"Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Mangazol, P.A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning ICML, New York, NY, USA.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gondara, L. (2016). Medical image denoising using convolutional denoising autoencoders. arXiv.","DOI":"10.1109\/ICDMW.2016.0041"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.jacr.2007.02.001","article-title":"Digital radiography image quality: Image processing and display","volume":"4","author":"Krupinski","year":"2007","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNRvs vs. SSIM. Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O. (2017). Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation. Bildverarbeitung f\u00fcr die Medizin 2017, Springer.","DOI":"10.1007\/978-3-662-54345-0_3"},{"key":"ref_30","unstructured":"Bradski, G., and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library, O\u2019Reilly Media."},{"key":"ref_31","unstructured":"Montgomery, D.C., and Runger, G.C. (2014). Applied Statistics and Probability for Engineers, Wiley. [6th ed.]. Cap. 10\u201311."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"396","DOI":"10.9734\/BJAST\/2015\/14975","article-title":"Likert scale: Explored and explained","volume":"7","author":"Joshi","year":"2015","journal-title":"Br. J. Appl. Sci. Technol."},{"key":"ref_33","first-page":"276","article-title":"Interrater reliability: The kappa statistic","volume":"22","author":"Mary","year":"2012","journal-title":"Biochem. Medica"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/9\/291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:33:06Z","timestamp":1760034786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/9\/291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"references-count":33,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["jimaging11090291"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11090291","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2025,8,26]]}}}