Computer-aided detection system for masses in automated whole breast ultrasonography: development and evaluation of the effectiveness.
- Author:
Jeoung Hyun KIM
1
;
Joo Hee CHA
;
Namkug KIM
;
Yongjun CHANG
;
Myung Su KO
;
Young Wook CHOI
;
Hak Hee KIM
Author Information
1. Department of Radiology, Ewha Womans University Mokdong Hospital, Ewha Womans University School of Medicine, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Computer-assisted radiographic image interpretation;
Mass screening;
Three-dimensional imaging
- MeSH:
Classification;
Humans;
Imaging, Three-Dimensional;
Mass Screening;
Radiographic Image Interpretation, Computer-Assisted;
Support Vector Machine;
Ultrasonography, Mammary*
- From:
Ultrasonography
2014;33(2):105-115
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: The aim of this study was to evaluate the performance of a proposed computer-aided detection (CAD) system in automated breast ultrasonography (ABUS). METHODS: Eighty-nine two-dimensional images (20 cysts, 42 benign lesions, and 27 malignant lesions) were obtained from 47 patients who underwent ABUS (ACUSON S2000). After boundary detection and removal, we detected mass candidates by using the proposed adjusted Otsu's threshold; the threshold was adaptive to the variations of pixel intensities in an image. Then, the detected candidates were segmented. Features of the segmented objects were extracted and used for training/testing in the classification. In our study, a support vector machine classifier was adopted. Eighteen features were used to determine whether the candidates were true lesions or not. A five-fold cross validation was repeated 20 times for the performance evaluation. The sensitivity and the false positive rate per image were calculated, and the classification accuracy was evaluated for each feature. RESULTS: In the classification step, the sensitivity of the proposed CAD system was 82.67% (SD, 0.02%). The false positive rate was 0.26 per image. In the detection/segmentation step, the sensitivities for benign and malignant mass detection were 90.47% (38/42) and 92.59% (25/27), respectively. In the five-fold cross-validation, the standard deviation of pixel intensities for the mass candidates was the most frequently selected feature, followed by the vertical position of the centroids. In the univariate analysis, each feature had 50% or higher accuracy. CONCLUSION: The proposed CAD system can be used for lesion detection in ABUS and may be useful in improving the screening efficiency.
