Open AccessComparison of Gradient-Based Edge Detectors Applied on Mammograms
Author(s) -
Cristiana Moroz-Dubenco
Publication year - 2021
Publication title -
studia universitatis babeş-bolyai. informatica
Language(s) - English
Resource type - Journals
eISSN - 2065-9601
pISSN - 1224-869X
DOI - 10.24193/subbi.2021.2.01
Subject(s) - sobel operator , prewitt operator , canny edge detector , computer science , edge detection , artificial intelligence , implementation , mammography , orientation (vector space) , computer vision , detector , pattern recognition (psychology) , image processing , image (mathematics) , breast cancer , mathematics , cancer , medicine , telecommunications , geometry , programming language
Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: pre-processing. That is, identifying the image orientation, detecting the breast and eliminating irrelevant parts.
This paper aims to describe, analyze, compare and evaluate six of the most commonly used edge detection operators: Sobel, Roberts Cross, Prewitt, Farid and Simoncelli, Scharr and Canny. We detail the algorithms, their implementations and the metrics used for evaluation and continue by comparing the operators both visually and numerically, finally concluding that Canny best suit our needs.