Computer-aided tumor detection in automated breast ultrasound images
Journal
Frontiers of Medical Imaging
Pages
279-298
Date Issued
2014
Author(s)
Lo, C.-M.
Abstract
Automated breast ultrasound (ABUS) is developed to automatically scan the whole breast for breast imaging on clinical examination. Hundreds of slices compose an image volume in a scanning to establish the volumetric structure of breasts. Reviewing the image volumes is a time-consuming task for radiologists. In this chapter, a computer-aided detection (CADe) system is proposed to automatically detect suspicious abnormalities in ABUS images. The database used included 122 abnormal and 37 normal cases. In abnormal cases, 58 are benign and 78 are malignant lesions. A Hessian-based multi-scale blob detection was used to segment blob-like structures into tumor candidates. The blobness, echogenicity, and morphology features were then extracted and combined in a logistic regression model to classify tumors and nontumors. The CADe system achieved the sensitivity of 100%, 90%, and 70% with false positives per case of 17.4, 8.8, and 2.7, respectively. The performance provides a promising use in tumor detection of ABUS images.
Type
book part