title: Classification of breast tumors using elastographic and B-mode features: comparison of automatic selection of representative slice and physician-selected slice of images.
abstract: Inter-observer variability and image quality are two key factors that can affect the diagnostic performance of elastography and B-mode ultrasound for breast tumor characterization. The purpose of this study is to use an image quantification method that automatically chooses a representative slice and then segments the tumor contour to evaluate the diagnostic features for tumor characterization. First, the representative slice is selected based on either the stiffness inside the tumor (the signal-to-noise ratio on the elastogram [SNRe]) or the contrast between the tumor and the surrounding normal tissue (the contrast-to-noise ratio on the elastogram [CNRe]). Next, the level set method is used to segment the tumor contour. Finally, the B-mode and elastographic features related to the segmented tumor are extracted for tumor characterization. The performance of the representative slice selected using the proposed methods is compared to that of the physician-selected slice in 151 biopsy-proven lesions (89 benign and 62 malignant). The diagnostic accuracies using elastographic features are 82.1% (124/151) for the slice with the maximum CNRe value, 82.1% (124/151) for the slice with the maximum SNRe value and 82.8% (125/151) for the physician-selected slice, whereas the diagnostic accuracies using B-mode features are 80.8% (122/151) for the slice with the maximum CNRe value, 87.4% (132/151) for the slice with the maximum SNRe value and 84.1% (127/151) for the physician-selected slice. When using both the B-mode and elastographic features to characterize the tumor, the accuracy of diagnosis is 86.1% (130/151) for the slice with the maximum CNRe value, 90.1% (136/151) for the slice with the maximum SNRe value and 89.4% (135/151) for the physician-selected slice. Our results show that the representative slice selected by SNRe and CNRe could be used to reduce the observer variability and to increase the diagnostic performance by the B-mode and elastographic features.
authors.affiliation: Department of Radiology, Seoul National University Hospital, Korea.
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
authors.forename: Shao-Chien
authors.initials: SC
authors.lastname: Chang
authors.forename: Jung Min
authors.initials: JM
authors.lastname: Chang
authors.forename: Nariya
authors.initials: N
authors.lastname: Cho
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.forename: Jen-Wei
authors.initials: JW
authors.lastname: Kuo
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology*; D054459:Elasticity Imaging Techniques / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015995:Prevalence; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013624:Taiwan / Q000453:epidemiology; D016217:Ultrasonography, Mammary / Q000706:statistics & numerical data*; D055815:Young Adult