Name
Abstract | ||
|---|---|---|
Digital mammograms are among the most difficult medical images to read, because of the differences in the types of tissues and their low contrasts. This paper proposes a computer aided diagnostic system for mammographic mass detection that can distinguish between tumorous and healthy tissue among various parenchymal tissue patterns. This method consists in extraction of regions of interest, noise elimination, global contrast improvement, combined segmentation, and rule-based classification. The evaluation of the proposed methodology is carried out on Mammography Image Analysis Society (MIAS) dataset. The achieved results increased the detection accuracy of the lesions and reduced the number of false diagnoses of mammograms. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26508-7_11 | AMBIENT INTELLIGENCE FOR HEALTH, AMIHEALTH 2015 |
Keywords | Field | DocType |
Mammogram enhancement,Mammogram segmentation,Breast mass detection,Image classification | Mammography,Computer vision,Diagnostic system,Segmentation,Computer-aided,Computer science,Noise elimination,Artificial intelligence,Contextual image classification,Medical diagnosis | Conference |
Volume | ISSN | Citations |
9456 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Victoria Carreras-Cruz | 1 | 0 | 0.34 |
| María de Lourdes Martínez-Villaseñor | 2 | 26 | 8.35 |
| Kevin Nataniel Rosas-Pérez | 3 | 0 | 0.34 |