Publication:
Automated breast profile segmentation for ROI detection using digital mammograms

Simple item page
dc.citedby99
dc.contributor.authorNagi J.en_US
dc.contributor.authorAbdul Kareem S.en_US
dc.contributor.authorNagi F.en_US
dc.contributor.authorKhaleel Ahmed S.en_US
dc.contributor.authorid25825455100en_US
dc.contributor.authorid9337499000en_US
dc.contributor.authorid56272534200en_US
dc.contributor.authorid25926812900en_US
dc.date.accessioned2023-12-28T07:17:45Z
dc.date.available2023-12-28T07:17:45Z
dc.date.issued2010
dc.description.abstractMammography 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5742205
dc.identifier.doi10.1109/IECBES.2010.5742205
dc.identifier.epage92
dc.identifier.scopus2-s2.0-79955421172
dc.identifier.spage87
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79955421172&doi=10.1109%2fIECBES.2010.5742205&partnerID=40&md5=9640d267ac8d1883fcc9f0dc7d18bfb4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29616
dc.pagecount5
dc.sourceScopus
dc.sourcetitleProceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
dc.subjectBreast cancer
dc.subjectMammogram segmentation
dc.subjectPectoral muscle
dc.subjectRegion of interest
dc.subjectSeeded region growing
dc.subjectAlgorithms
dc.subjectBiomedical engineering
dc.subjectDiseases
dc.subjectImage segmentation
dc.subjectMuscle
dc.subjectX ray screens
dc.subjectBreast Cancer
dc.subjectMammogram segmentation
dc.subjectPectoral muscle
dc.subjectRegion of interest
dc.subjectSeeded region growing
dc.subjectMammography
dc.titleAutomated breast profile segmentation for ROI detection using digital mammogramsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS