Paper Info
Title
A New Benchmark And Method For The Evaluation Of Chest Wall Detection In Digital Mammography
Abstract
CAD systems have shown good potential for improving breast cancer diagnosis and anomaly detection in mammograms. A basic enabling step for the utilization of CAD systems in mammographic analysis is the correct identification of the breast region. Therefore, several methods to segment the pectoral muscle in the medio-lateral oblique (MLO) mammographic view have been proposed in the literature. However, currently it is difficult to perform and objective comparison between different chest wall (CW) detection methods since they are often evaluated with different evaluation procedures, datasets and the implementations of the methods are not publicly available. For this reason, we propose a methodology to evaluate and compare the performance of CW detection methods using a publicly available dataset (INbreast). We also propose a new intensity-based method for automatic CW detection. We then utilize the proposed evaluation methodology to compare the performance of our CW detection algorithm with a state-of-the-art CW detection method. The performance was measured in terms of the Dice's coefficient similarity, the area error and mean contour distance. The proposed method achieves yielded the best results in all the performance measures.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9175960
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Gerson Africano100.68
Otso Arponen200.68
Antti Sassi300.68
Irina Rinta-Kiikka400.68
Anna-Leena Laaperi500.68
Said Pertuz6796.37