ArticleJournal of medical and biological engineering
Shape-based Automatic Detection of Pectoral Muscle Boundary in Mammograms.
Article in Journal of medical and biological engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 18 citations in OpenAlex.
- Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.Journal of digital imaging · 2018Article
- Article
- Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.Journal of medical systems · 2017Article
- Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.Journal of medical systems · 2016Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The detection of the pectoral muscle boundary in the medio-lateral oblique view of mammograms is essential to improving the computer-aided diagnosis of breast cancer. In this study, a shape-based detection method is proposed for accurately extracting the boundary of the pectoral muscle in mammograms. A shape-based enhancement mask is applied to the mammogram and the initial boundary is then defined using morphological operators. The seed point is then detected on the initial boundary and the pectoral boundary is evolved from candidate points produced using a shape-based growth strategy. A cubic polynomial fitting function is implemented to obtain the final pectoral muscle boundary. The proposed method was applied to 322 mammograms from the mini Mammographic Image Analysis Society database. A 97.2 % acceptable rate from expert radiologists and assessment results based on the false positive rate, false negative rate, and Hausdorff distance demonstrate the robustness and effectiveness of the proposed shape-based detection method.
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