Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010 |
| Pages | 87-92 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 01 Dec 2010 |
| Event | 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010 - Kuala Lumpur, Malaysia Duration: 30 Nov 2010 → 02 Dec 2010 |
Publication series
| Name | Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010 |
|---|
Other
| Other | 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010 |
|---|---|
| Country | Malaysia |
| City | Kuala Lumpur |
| Period | 30/11/10 → 02/12/10 |
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All Science Journal Classification (ASJC) codes
- Biomedical Engineering
Cite this
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Automated breast profile segmentation for ROI detection using digital mammograms. / Nagi, Jawad; Abdul Kareem, Sameem; Nagi, Farrukh; Khaleel Ahmed, Syed.
Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010. 2010. p. 87-92 5742205 (Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Automated breast profile segmentation for ROI detection using digital mammograms
AU - Nagi, Jawad
AU - Abdul Kareem, Sameem
AU - Nagi, Farrukh
AU - Khaleel Ahmed, Syed
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79955421172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955421172&partnerID=8YFLogxK
U2 - 10.1109/IECBES.2010.5742205
DO - 10.1109/IECBES.2010.5742205
M3 - Conference contribution
SN - 9781424476008
T3 - Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
SP - 87
EP - 92
BT - Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
ER -