Enhancing Mammogram Images with Segmentation and Colorization for Assisting Breast Cancer Detection

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dc.contributor.authorTien Pham, William
dc.contributor.authorTat Pham, Trung
dc.contributor.authorIllescas Maldonado, Pamela
dc.date.accessioned2021-01-13T16:55:36Z
dc.date.available2021-01-13T16:55:36Z
dc.date.issued2020
dc.description.abstractThis paper presents a combined sequence of the K-mean clustering of mammogram images to identify the region of interest and false colorization of the region of interest to enhance digital mammograms in the context of assisting the analysis for the detection of breast cancer. The K-means clustering technique was selected for its computational efficiency and for its not requiring prior knowledge of the statistical nature of the data. The region of interest is selected among the resulting clusters so that it can be colorized in a magnifying manner along a digitally simulated visible spectrum to enhance visualization of details that is so important for medical experts in their detection of breast cancer. Numerical results are presented in the study as the proof of concept, and demonstration of workability, applicability, and practicality of the newly developed sequential hybrid technique of image enhancement.es_ES
dc.identifier.issn2473-554X
dc.identifier.other10.19080/CTOIJ.2020.17.555960
dc.identifier.urihttps://hdl.handle.net/20.500.12536/1214
dc.language.isoeses_ES
dc.sourceCancer Therapy & Oncology International Journales_ES
dc.subjectImage enhancementes_ES
dc.subjectSegmentationes_ES
dc.subjectColorizationes_ES
dc.subjectMammogramses_ES
dc.subjectBreast canceres_ES
dc.titleEnhancing Mammogram Images with Segmentation and Colorization for Assisting Breast Cancer Detectiones_ES
dc.typeArtículo de revistaes_ES
uvm.carreraTecnología Médicaes_ES
uvm.escuelaEscuela de Ciencias de la Saludes_ES
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