Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4958176 | Computer Methods and Programs in Biomedicine | 2017 | 32 Pages |
Abstract
Results shows that the breast area can be discriminated from the pectoral-muscle by avoiding to work with brightness areas that produces false positives. Moreover, because the image size is reduced the computer processing time will be decreased. This segmentation stage can be an addition to mammograms analysis broadly, not only to find mcc but abnormalities such as circumscribed masses, speculated masses and architectural distortion. Also is useful to create automatically an unsupervised segmentation in mammograms without stage of training.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Luis Antonio Salazar-Licea, Jesús Carlos Pedraza-Ortega, Alberto Pastrana-Palma, Marco A. Aceves-Fernandez,