Automated adenocarcinoma lung cancer tissue images segmentation based on clustering
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| Autores: | , |
|---|---|
| Formáid: | artículo original |
| Stádas: | Versión publicada |
| Fecha de Publicación: | 2022 |
| Cur Síos: | Cancer is one of the main dead causes worldwide. It is re- sponsible for an approximate of 1 out of 6 deaths globally and lung cancer is along breast cancer, the most common types of cancer in the population, which confirms the importance of studies associated with it. This work presents an approach toward lung cancer histological tissue images segmentation based on colour. The proposed method for the segmentation is K-means clustering, providing promising results that may become as an assistance for pathologists, as it can help them reduce the time consumed reviewing the slides and giving a more objective perspective in order to provide a diagnose and specific treatment. |
| País: | Portal de Revistas TEC |
| Institiúid: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Teanga: | Inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/6442 |
| Rochtain Ar Líne: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/6442 |
| Palabra clave: | Digital pathology pattern recognition lung cancer Patología digital reconocimiento de patrones cáncer de pulmón |