ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Segmentation of the Breast Region in Mammograms Using Snakes
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
A new approach for breast skin-line estimation in mammograms
Pattern Analysis & Applications
Accurate Breast Region Detection in Digital Mammograms Using a Local Adaptive Thresholding Method
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Computer Methods and Programs in Biomedicine
Automated assessment of breast tissue density in digital mammograms
Computer Vision and Image Understanding
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram
Computers in Biology and Medicine
Segmentation of regions of interest in mammograms in a topographic approach
IEEE Transactions on Information Technology in Biomedicine
Breast segmentation with pectoral muscle suppression on digital mammograms
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
Least squares quantization in PCM
IEEE Transactions on Information Theory
Estimation of the breast skin-line in mammograms using multidirectional Gabor filters
Computers in Biology and Medicine
Saliency based mass detection from screening mammograms
Signal Processing
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Accurate segmentation of the breast from digital mammograms is an important pre-processing step for computerized breast cancer detection. In this study, we propose a fully automated segmentation method. Noise on the acquired mammogram is reduced by median filtering; multidirectional scanning is then applied to the resultant image using a moving window 15x1 in size. The border pixels are detected using the intensity value and maximum gradient value of the window. The breast boundary is identified from the detected pixels filtered using an averaging filter. The segmentation accuracy on a dataset of 84 mammograms from the MIAS database is 99%.