Open AccessAn Introspective Performance Analysis of Threshold-based Segmentation Techniques on Digital Mammograms
Author(s) -
A. Nithya,
P Shanmugavadivu
Publication year - 2021
Publication title -
ymer
Language(s) - English
Resource type - Journals
ISSN - 0044-0477
DOI - 10.37896/ymer20.11/16
Subject(s) - segmentation , thresholding , artificial intelligence , computer science , image segmentation , computer vision , otsu's method , pattern recognition (psychology) , image processing , mammography , region of interest , image (mathematics) , breast cancer , cancer , medicine
Image segmentation, as a pre-processing step, plays a vital role in medical image analysis. The variants of threshold-based image segmentation methods are proved to offer feasible and optimal solutions to extract the region of interest (RoI), from medical images. Digital mammograms are used as a reliable source of breast cancer prognosis and diagnosis. Thresholding is a simple and effective strategy that finds applications in image processing and analysis. This research aimed to analyze the performance behaviour of a few threshold-based segmentation methods with respect to the intensity distribution of the input mammograms. For this analytical research, six automated thresholding segmentation techniques were chosen: Kapur, Otsu’s, Isoentropic, Ridler & Calvard’s, Kittler & Illingworth's, and Yen. The performance and behaviour of those methods were validated on the digital mammogram images of mini-MIAS database featured with Fatty (F), Fatty-Glandular (G), and Dense-Glandular (D) breast tissues. Those methods were analyzed on two metrics viz., Region Non-Uniformity (RNU) and computation time. The results of this research confirm that Ridler & Calvard’s method gives the best segmentation results for Dense-Glandular, Isoentropic method gives better segmentation results for Fatty and Yen method works well on the Fatty-Glandular normal mammogram images.