title: Computerized characterization of breast masses on three-dimensional ultrasound volumes.
abstract: We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109-0904, USA. berki@umich.edu
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Marilyn A
authors.initials: MA
authors.lastname: Roubidoux
authors.forename: Mark A
authors.initials: MA
authors.lastname: Helvie
authors.forename: Lubomir M
authors.initials: LM
authors.lastname: Hadjiiski
authors.forename: Aditya
authors.initials: A
authors.lastname: Ramachandran
authors.forename: Chintana
authors.initials: C
authors.lastname: Paramagul
authors.forename: Gerald L
authors.initials: GL
authors.lastname: LeCarpentier
authors.forename: Alexis
authors.initials: A
authors.lastname: Nees
authors.forename: Caroline
authors.initials: C
authors.lastname: Blane
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*