Computer-aided multiview tumor detection for automated whole breast ultrasound
Journal
Ultrasonic Imaging
Journal Volume
36
Journal Issue
1
Pages
3-17
Date Issued
2014-01
Author(s)
Abstract
Automated whole breast ultrasound (ABUS) has become a popular screening tool in recent years. To reduce the review time and misdetection from ABUS images by physicians, a computer-aided detection (CADe) system for ABUS images based on a multiview method is proposed in this study. A total of 58 pathology-proven lesions from 41 patients were used to evaluate the performance of the system. In the proposed CADe system, the fuzzy c-mean clustering method was applied to detect tumor candidates from these ABUS images. Subsequently, the tumor likelihoods of these candidates could be estimated by a logistic linear regression model based on the intensity, morphology, location, and size features in the transverse, longitudinal, and coronal views. Finally, the multiview tumor likelihoods of the tumor candidates could be obtained from the estimated tumor likelihoods of the three views, and the tumor candidates with high multiview tumor likelihoods were regarded as the detected tumors in the proposed system. The sensitivities of the multiview tumor detection for selecting 5, 10, 20, and 30 tumor candidates with the largest multiview tumor likelihoods were 79.31%, 86.21%, 96.55%, and 98.28%, respectively. ? The Author(s) 2013.
Subjects
automated whole breast ultrasound
breast cancer
computer-aided detection
fuzzy c-means
multiview detection
SDGs
Other Subjects
Automation; Logistic regression; Tumors; Ultrasonic applications; Breast Cancer; Breast ultrasound; Computer aided detection; Fuzzy C mean; Multi-view detection; Diagnosis; article; automated whole breast ultrasound; breast cancer; breast tumor; cluster analysis; computer-aided detection; echography; echomammography; female; fuzzy c-means; fuzzy logic; human; image processing; methodology; multiview detection; reproducibility; sensitivity and specificity; statistics; automated whole breast ultrasound; breast cancer; computer-aided detection; fuzzy c-means; multiview detection; Breast Neoplasms; Cluster Analysis; Female; Fuzzy Logic; Humans; Image Processing, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography, Mammary
Publisher
SAGE Publications Inc.
Type
journal article