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dc.creatorArikidis, N.en
dc.creatorVassiou, K.en
dc.creatorKazantzi, A.en
dc.creatorSkiadopoulos, S.en
dc.creatorKarahaliou, A.en
dc.creatorCostaridou, L.en
dc.date.accessioned2015-11-23T10:22:51Z
dc.date.available2015-11-23T10:22:51Z
dc.date.issued2015
dc.identifier10.1118/1.4930246
dc.identifier.issn942405
dc.identifier.urihttp://hdl.handle.net/11615/25812
dc.description.abstractPurpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologist's segmentations quantitatively by two distance metrics (Hausdorff distance - HDISTcluster, average of minimum distance - MINDISTcluster) and the area overlap measure (AOMcluster). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az Standard Error) utilizing tenfold cross-validation methodology. A previously developed B-spline active rays segmentation method was also considered for comparison purposes. Results: Interobserver and intraobserver segmentation agreements (median and [25%, 75%] quartile range) were substantial with respect to the distance metrics HDISTcluster (2.3 [1.8, 2.9] and 2.5 [2.1, 3.2] pixels) and AMINDISTcluster (0.8 [0.6, 1.0] and 1.0 [0.8, 1.2] pixels), while moderate with respect to AOMcluster (0.64 [0.55, 0.71] and 0.59 [0.52, 0.66]). The proposed segmentation method outperformed (0.800.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.690.04), suggesting the significance of the proposed semiautomated method. Conclusions: Results indicate a reliable semiautomated segmentation method for MC clusters offered by deformable models, which could be utilized in MC cluster quantitative image analysis. © 2015 American Association of Physicists in Medicine.en
dc.sourceMedical Physicsen
dc.source.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-84941959334&partnerID=40&md5=ce7c90b6aaf73dc069535f69a56cd248
dc.subjectactive contoursen
dc.subjectdiagnostic accuracyen
dc.subjectlevel seten
dc.subjectmammographyen
dc.subjectmicrocalcification cluster segmentationen
dc.subjectscale-space representationen
dc.subjectsegmentation reliabilityen
dc.subjectanatomical conceptsen
dc.subjectArticleen
dc.subjectbreast calcificationen
dc.subjectbreast densityen
dc.subjectclassificationen
dc.subjectcomputer assisted diagnosisen
dc.subjectcontrolled studyen
dc.subjecthumanen
dc.subjectimage analysisen
dc.subjectimage displayen
dc.subjectimage processingen
dc.subjectmajor clinical studyen
dc.subjectreliabilityen
dc.subjectsupport vector machineen
dc.titleA two-stage method for microcalcification cluster segmentation in mammography by deformable modelsen
dc.typejournalArticleen


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