دورية أكاديمية
A two-stage method for microcalcification cluster segmentation in mammography by deformable models
| العنوان: | A two-stage method for microcalcification cluster segmentation in mammography by deformable models |
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| المؤلفون: | Arikidis, N., Vassiou, K., Kazantzi, A., Skiadopoulos, S., Karahaliou, A., Costaridou, L. |
| المصدر: | Medical Physics ; http://www.scopus.com/inward/record.url?eid=2-s2.0-84941959334&partnerID=40&md5=ce7c90b6aaf73dc069535f69a56cd248Test |
| سنة النشر: | 2015 |
| المجموعة: | University of Thessaly Institutional Repository / Ιδρυματικό Αποθετήριο Πανεπιστημίου Θεσσαλίας |
| مصطلحات موضوعية: | active contours, diagnostic accuracy, level set, mammography, microcalcification cluster segmentation, scale-space representation, segmentation reliability, anatomical concepts, Article, breast calcification, breast density, classification, computer assisted diagnosis, controlled study, human, image analysis, image display, image processing, major clinical study, reliability, support vector machine |
| الوصف: | Purpose: 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 ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| العلاقة: | 942405; http://hdl.handle.net/11615/25812Test |
| DOI: | 10.1118/1.4930246 |
| الإتاحة: | https://doi.org/10.1118/1.4930246Test http://hdl.handle.net/11615/25812Test |
| رقم الانضمام: | edsbas.3500DCD0 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1118/1.4930246 |
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