A Novel Bio-Inspired Method for Early Diagnosis of Breast Cancer through Mammographic Image Analysis

Applied Sciences

Yenny Villuendas-Rey¹  Amadeo-José Argüelles-Cruz²  David González-Patiño²  Fakhri Karray³ 

[1] Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional, Ciudad de México 07700, Mexico;Centro de Investigación en Computación, Instituto Politécnico Nacional, Ciudad de México 07738, Mexico;Department of Electrical and Computer Engineering, University of Waterloo, Ontario, ON N2L 3G1, Canada;

关键词: mammogram;    meta-heuristics;    optimization;    breast cancer;    segmentation;    detection;   

DOI  :  10.3390/app9214492

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown