RICE: A method for quantitative mammographic image enhancement

Janan, Faraz and Brady, Michael (2021) RICE: A method for quantitative mammographic image enhancement. Medical Image Analysis, 71 . p. 102043. ISSN 1361-8415

Full content URL: https://doi.org/10.1016/j.media.2021.102043

Documents
RICE: A method for quantitative mammographic image enhancement
Authors' Accepted Manuscript

Request a copy
[img] PDF
RICE Media Paper - Final revisions 08032021.pdf - Whole Document
Restricted to Repository staff only until 26 March 2022.

1MB
Item Type:Article
Item Status:Live Archive

Abstract

We introduce Region of Interest Contrast Enhancement (RICE) to identify focal densities in mammograms. It aims to help radiologists: 1) enhancing the contrast of mammographic images; and 2) detecting re- gions of interest (such as focal densities) that are candidate masses potentially masked behind dense parenchyma. Cancer masking is an unsolved issue, particularly in breast density categories BI-RADS C and D. RICE suppresses normal breast parenchyma in order to highlight focal densities. Unlike methods that enhance mammograms by modifying the dynamic range of an image; RICE relies on the actual tis- sue composition of the breast. It segments Volumetric Breast Density (VBD) maps into smaller regions and then applies a recursive mechanism to estimate the ‘neighbourhood’ for each segment. The method then subtracts and updates the neighbourhood, or the encompassing tissue, from each piecewise con- stant component of the breast image. This not only enhances the appearance of a candidate mass but also helps in estimating the mass density. In extensive experiments, RICE enhances focal densities in all breast density types including the most challenging category BI-RADS D. Suitably adapted, RICE can be used as a precursor to any computer-aided diagnostics and detection system.

Keywords:Image Enhancement, Contrast Enhancement, Breast Cancer, Cancer Masking, Breast Density, Focal Density
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:44509
Deposited On:12 Apr 2021 11:05

Repository Staff Only: item control page