Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximuma posteriorimodel-Reference-Cited by-同舟云学术

Segmenting pectoralis muscle on digital mammograms by a Markov random field-maximuma posteriorimodel

Published:2014-11-25 Issue:3 Volume:1 Page:034503
ISSN:2329-4302
Container-title:Journal of Medical Imaging
language:en
Short-container-title:J. Med. Imag

Author:

Ge Mei¹,Mainprize James G.¹,Mawdsley Gordon E.¹,Yaffe Martin J.²

Affiliation:

1. Sunnybrook Research Institute, Department of Physical Sciences, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada

2. Sunnybrook Research Institute, Department of Physical Sciences, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, CanadabUniversity of Toronto, Department of Medical Biophysics, Toronto, Ontario M5G 1L7, Canada

Publisher

SPIE-Intl Soc Optical Eng

Subject

Radiology Nuclear Medicine and imaging

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1. A Data-Centric Approach for Pectoral Muscle Deep Learning Segmentation Enhancements in Mammography Images;Advances in Visual Computing;2023

2. Full‐field digital mammography: the ‘30% rule’ and influences on visualisation of the pectoralis major muscle on the craniocaudal view of the breast;Journal of Medical Radiation Sciences;2020-06-22

3. Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review;Journal of Medical Internet Research;2019-07-26

4. Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography;Medical Physics;2017-06-09

5. Segmentation of pectoral muscle using the adaptive gamma corrections;Multimedia Tools and Applications;2017-01-06