Computer-Aided Diagnosis

Quantitative analysis of ultrasound images for computer-aided diagnosis

[+] Author Affiliations
Jie Ying Wu

Brown University, School of Engineering, 82 Hope Street, Providence, Rhode Island 02912, United States

Adam Tuomi, David Glidden

Brown University, Alpert Medical School, 222 Richmond Street, Providence, Rhode Island 02903, United States

Michael D. Beland, Joseph Konrad, David Grand, Derek Merck

Rhode Island Hospital, Department of Diagnostic Imaging, 593 Eddy Street, Providence, Rhode Island 02903, United States

J. Med. Imag. 3(1), 014501 (Jan 25, 2016). doi:10.1117/1.JMI.3.1.014501
History: Received June 10, 2015; Accepted December 18, 2015
Text Size: A A A

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.

Figures in this Article
© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Jie Ying Wu ; Adam Tuomi ; Michael D. Beland ; Joseph Konrad ; David Glidden, et al.
"Quantitative analysis of ultrasound images for computer-aided diagnosis", J. Med. Imag. 3(1), 014501 (Jan 25, 2016). ; http://dx.doi.org/10.1117/1.JMI.3.1.014501


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.