TY - JOUR
T1 - A Novel bio-inspired method for early diagnosis of breast cancer through mammographic image analysis
AU - González-Patiño, David
AU - Villuendas-Rey, Yenny
AU - Argüelles-Cruz, Amadeo José
AU - Karray, Fakhri
N1 - Publisher Copyright:
© 2019 by the authors.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn's index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method.
AB - Breast cancer is a current problem that causes the death of many women. In this work, we test meta-heuristics applied to the segmentation of mammographic images. Traditionally, the application of these algorithms has a direct relationship with optimization problems; however, in this study, its implementation is oriented to the segmentation of mammograms using the Dunn index as an optimization function, and the grey levels to represent each individual. The update of grey levels during the process results in the maximization of the Dunn's index function; the higher the index, the better the segmentation will be. The results showed a lower error rate using these meta-heuristics for segmentation compared to a well-adopted classical approach known as the Otsu method.
KW - Breast cancer
KW - Detection
KW - Mammogram
KW - Meta-heuristics
KW - Optimization
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85075212204&partnerID=8YFLogxK
U2 - 10.3390/app9214492
DO - 10.3390/app9214492
M3 - Artículo
SN - 2076-3417
VL - 9
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 21
M1 - 4492
ER -