title: A two-stage method for microcalcification cluster segmentation in mammography by deformable models.
abstract: OBJECTIVE
  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 radiologists' segmentations quantitatively by two distance metrics (Hausdorff distance-HDISTcluster, average of minimum distance-AMINDISTcluster) 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 (0.80 ± 0.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.69 ± 0.04), 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.
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Patras 26504, Greece.
authors.forename: N
authors.initials: N
authors.lastname: Arikidis
authors.affiliation: Department of Anatomy, School of Medicine, University of Thessaly, Larissa 41500, Greece.
authors.forename: K
authors.initials: K
authors.lastname: Vassiou
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Patras 26504, Greece.
authors.forename: A
authors.initials: A
authors.lastname: Kazantzi
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Patras 26504, Greece.
authors.forename: S
authors.initials: S
authors.lastname: Skiadopoulos
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Patras 26504, Greece.
authors.forename: A
authors.initials: A
authors.lastname: Karahaliou
authors.affiliation: Department of Medical Physics, School of Medicine, University of Patras, Patras 26504, Greece.
authors.forename: L
authors.initials: L
authors.lastname: Costaridou
mesh_terms: D002114:Calcinosis / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography; D008962:Models, Theoretical