title: Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.
abstract: Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor 48109-0904, USA. berki@umich.edu
authors.forename: B
authors.initials: B
authors.lastname: Sahiner
authors.forename: N
authors.initials: N
authors.lastname: Petrick
authors.forename: H P
authors.initials: HP
authors.lastname: Chan
authors.forename: L M
authors.initials: LM
authors.lastname: Hadjiiski
authors.forename: C
authors.initials: C
authors.lastname: Paramagul
authors.forename: M A
authors.initials: MA
authors.lastname: Helvie
authors.forename: M N
authors.initials: MN
authors.lastname: Gurcan
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D016208:Databases, Factual; D003937:Diagnosis, Differential; D005189:False Positive Reactions; D006801:Humans; D008327:Mammography / Q000145:classification* / Q000379:methods* / Q000706:statistics & numerical data; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011897:Random Allocation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity