| dc.contributor.author | Mello Román, Julio César | |
| dc.contributor.author | Escobar Torres, Ricardo Daniel | |
| dc.contributor.author | Martínez Martínez, Fabiola Beatriz | |
| dc.contributor.author | Vázquez Noguera, José Luis | |
| dc.contributor.author | Legal Ayala, Horacio Andrés | |
| dc.contributor.author | Pinto Roa, Diego Pedro | |
| dc.contributor.other | Universidad Nacional de Asunción. Facultad Politécnica | es |
| dc.date.accessioned | 2025-06-11T15:34:15Z | |
| dc.date.available | 2025-06-11T15:34:15Z | |
| dc.date.issued | 2020-07-31 | |
| dc.identifier.citation | Mello Román, J. C., Escobar, R., Martínez, F., Vázquez Noguera, J. L., Legal-Ayala, H., & Pinto-Roa, D. P. (2020). Medical image enhancement with brightness and detail preserving using multiscale top-hat transform by reconstruction. Electronic Notes in Theoretical Computer Science, 349, 69-80.
https://doi.org/10.1016/j.entcs.2020.02.013 | en |
| dc.identifier.issn | 1571-0661 | es |
| dc.identifier.other | https://doi.org/10.1016/j.entcs.2020.02.013 | es |
| dc.identifier.uri | http://hdl.handle.net/20.500.14066/4589 | |
| dc.description.abstract | Medical imaging help medical doctors provide faster and more efficient diagnoses to their patients. Medical image quality directly influences diagnosis. However, when medical images are acquired, they often present degradations such as poor detail or low contrast. This work presents an algorithm that improves contrast and detail, preserving the natural brightness of medical images. The proposed method is based on multiscale top-hat transform by reconstruction. It extracts multiple features from the image that are then used to enhance the medical image. To quantify the performance of the proposed method, 100 medical images from a public database were used. Experiments show that the proposal improves contrast, introducing less distortion and preserving the average brightness of medical images. | es |
| dc.description.sponsorship | Consejo Nacional de Ciencia y Tecnología | es |
| dc.language.iso | eng | es |
| dc.publisher | Elsevier | es |
| dc.rights | Atribución 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject.other | Low contrast | es |
| dc.subject.other | Medical imaging | es |
| dc.subject.other | Multiscale top-hat transform by reconstruction | es |
| dc.subject.other | Natural brightness | es |
| dc.title | Medical image enhancement with brightness and detail preserving using multiscale top-hat transform by reconstruction | es |
| dc.type | info:eu-repo/semantics/article | es |
| dc.type | info:eu-repo/semantics/publishedVersion | es |
| dc.identifier.doi | 10.1016/j.entcs.2020.02.013 | es |
| dc.description.fundingtext | Programa Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de creación y fortalecimiento de maestrías y doctorados de excelencia | es |
| dc.journal.title | Electronic Notes in Theoretical Computer Science | es |
| dc.page.initial | 69 | es |
| dc.page.final | 80 | es |
| dc.relation.projectCONACYT | POSG17-53 | es |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
| dc.rights.copyright | © 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | es |
| dc.volume.number | 349 | es |