ArticleBioMed research international2016
Automatic Detection of Pectoral Muscle Region for Computer-Aided Diagnosis Using MIAS Mammograms.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 45 citations in OpenAlex.
- Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers.Journal of clinical medicine · 2026Article
- Towards Robust Supervised Pectoral Muscle Segmentation in Mammography Images.Journal of imaging · 2024Article
- Sharing Data Is Essential for the Future of AI in Medical Imaging.Radiology. Artificial intelligence · 2024Article
- Mammography with deep learning for breast cancer detection.Frontiers in oncology · 2024Review
- Comparison between two packages for pectoral muscle removal on mammographic images.La Radiologia medica · 2022Article
- Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer.Healthcare (Basel, Switzerland) · 2022Article
- Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms.Healthcare informatics research · 2021Article
- Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.Journal of digital imaging · 2018Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The computer-aided detection (CAD) systems have been developed to help radiologists with the early detection of breast cancer. This system provides objective and accurate information to reduce the misdiagnosis of the disease. In mammography, the pectoral muscle region is used as an index to compare the symmetry between the left and right images in the mediolateral oblique (MLO) view. The pectoral muscle segmentation is necessary for the detection of microcalcification or mass because the pectoral muscle has a similar pixel intensity as that of lesions, which affects the results of automatic detection. In this study, the mammographic image analysis society database (MIAS, 322 cases) was used for detecting the pectoral muscle segmentation. The pectoral muscle was detected by using the morphological method and the random sample consensus (RANSAC) algorithm. We evaluated the detected pectoral muscle region and compared the manual segmentation with the automatic segmentation. The results showed 92.2% accuracy. We expect that the proposed method improves the detection accuracy of breast cancer lesions using a CAD system.
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