Comparison of Gradient-Based Edge Detectors Applied on Mammograms

Abstract

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. Received by the editors: 6 June 2021. 2010 Mathematics Subject Classification. 68U10. 1998 CR Categories and Descriptors. I.4.6 [Image Processing and Computer Vision]:Segmentation – Edge and feature detection.

Keywords

• Image Processing, Computer Vision, Edge Detection.