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Journal of Medical Imaging

Quantitative analysis of ultrasound images for computer-aided diagnosis
Author(s): Jie Ying Wu; Adam Tuomi; Michael D. Beland; Joseph Konrad; David Glidden; David Grand; Derek Merck
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Paper Abstract

We propose an adaptable framework for analyzing ultrasound (US) images quantitatively to provide computer-aided diagnosis using machine learning. Our preliminary clinical targets are hepatic steatosis, adenomyosis, and craniosynostosis. For steatosis and adenomyosis, we collected US studies from 288 and 88 patients, respectively, as well as their biopsy or magnetic resonanceconfirmed diagnosis. Radiologists identified a region of interest (ROI) on each image. We filtered the US images for various texture responses and use the pixel intensity distribution within each ROI as feature parameterizations. Our craniosynostosis dataset consisted of 22 CT-confirmed cases and 22 age-matched controls. One physician manually measured the vectors from the center of the skull to the outer cortex at every 10 deg for each image and we used the principal directions as shape features for parameterization. These parameters and the known diagnosis were used to train classifiers. Testing with cross-validation, we obtained 72.74% accuracy and 0.71 area under receiver operating characteristics curve for steatosis (p<0.0001), 77.27% and 0.77 for adenomyosis (p<0.0001), and 88.63% and 0.89 for craniosynostosis (p=0.0006). Our framework is able to detect a variety of diseases with high accuracy. We hope to include it as a routinely available support system in the clinic.

Paper Details

Date Published: 25 January 2016
PDF: 9 pages
J. Med. Img. 3(1) 014501 doi: 10.1117/1.JMI.3.1.014501
Published in: Journal of Medical Imaging Volume 3, Issue 1
Show Author Affiliations
Jie Ying Wu, Brown Univ. (United States)
Adam Tuomi, Brown Univ. (United States)
Michael D. Beland, Rhode Island Hospital (United States)
Joseph Konrad, Rhode Island Hospital (United States)
David Glidden, Brown Univ. (United States)
David Grand, Rhode Island Hospital (United States)
Derek Merck, Rhode Island Hospital (United States)

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