Automated breast profile segmentation for ROI detection using digital mammograms
Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region an...
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2023
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| author | Nagi J. Abdul Kareem S. Nagi F. Khaleel Ahmed S. |
| author2 | 25825455100 |
| author_facet | 25825455100 Nagi J. Abdul Kareem S. Nagi F. Khaleel Ahmed S. |
| author_sort | Nagi J. |
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| content_provider | Universiti Tenaga Nasional |
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| continent | Asia |
| country | Malaysia |
| description | Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region. To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics. Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images. � 2010 IEEE. |
| format | Conference Paper |
| id | my.uniten.dspace-29616 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| record_format | dspace |
| spelling | my.uniten.dspace-296162024-04-18T09:25:28Z Automated breast profile segmentation for ROI detection using digital mammograms Nagi J. Abdul Kareem S. Nagi F. Khaleel Ahmed S. 25825455100 9337499000 56272534200 25926812900 Breast cancer Mammogram segmentation Pectoral muscle Region of interest Seeded region growing Algorithms Biomedical engineering Diseases Image segmentation Muscle X ray screens Breast Cancer Mammogram segmentation Pectoral muscle Region of interest Seeded region growing Mammography Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection. Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram. The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing. In this paper we explore an automated technique for mammogram segmentation. The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region. To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics. Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images. � 2010 IEEE. Final 2023-12-28T07:17:45Z 2023-12-28T07:17:45Z 2010 Conference Paper 10.1109/IECBES.2010.5742205 2-s2.0-79955421172 https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955421172&doi=10.1109%2fIECBES.2010.5742205&partnerID=40&md5=9640d267ac8d1883fcc9f0dc7d18bfb4 https://irepository.uniten.edu.my/handle/123456789/29616 5742205 87 92 Scopus |
| spellingShingle | Breast cancer Mammogram segmentation Pectoral muscle Region of interest Seeded region growing Algorithms Biomedical engineering Diseases Image segmentation Muscle X ray screens Breast Cancer Mammogram segmentation Pectoral muscle Region of interest Seeded region growing Mammography Nagi J. Abdul Kareem S. Nagi F. Khaleel Ahmed S. Automated breast profile segmentation for ROI detection using digital mammograms |
| title | Automated breast profile segmentation for ROI detection using digital mammograms |
| title_full | Automated breast profile segmentation for ROI detection using digital mammograms |
| title_fullStr | Automated breast profile segmentation for ROI detection using digital mammograms |
| title_full_unstemmed | Automated breast profile segmentation for ROI detection using digital mammograms |
| title_short | Automated breast profile segmentation for ROI detection using digital mammograms |
| title_sort | automated breast profile segmentation for roi detection using digital mammograms |
| topic | Breast cancer Mammogram segmentation Pectoral muscle Region of interest Seeded region growing Algorithms Biomedical engineering Diseases Image segmentation Muscle X ray screens Breast Cancer Mammogram segmentation Pectoral muscle Region of interest Seeded region growing Mammography |
| url_provider | http://dspace.uniten.edu.my/ |