ArticleJournal of digital imaging2016
Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression.
Article in Journal of digital imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Distribution of lower limb muscles may be associated with the progression of knee osteoarthritis and sarcopenia: a cross-sectional study.Frontiers in medicine · 2026Article
- Uncertainty-Aware Semi-Supervised Method for Pectoral Muscle Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model.Diagnostics (Basel, Switzerland) · 2024Article
- Identification of breast lesion through integrated study of gorilla troops optimization and rotation-based learning from MRI images.Scientific reports · 2023Article
- The application of traditional machine learning and deep learning techniques in mammography: a review.Frontiers in oncology · 2023Review
- Comparison between two packages for pectoral muscle removal on mammographic images.La Radiologia medica · 2022Article
- Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm.BioMed research international · 2022Article
- Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.Journal of medical systems · 2017Article
- Breast Tissue Organisation and its Association with Breast Cancer Risk.Breast cancer research : BCR · 2017Article
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3 authors.
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Abstract
In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases-including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.
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