Quantitative imaging features: extension of the oncology medical image database

M. N. Patel; P. T. Looney; K. C. Young; M. D. Halling-Brown

doi:10.1117/12.2082114

17 March 2015 Quantitative imaging features: extension of the oncology medical image database

M. N. Patel, P. T. Looney, K. C. Young, M. D. Halling-Brown

Proceedings Volume 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations; 941812 (2015) https://doi.org/10.1117/12.2082114
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes.

Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Citation Download Citation

M. N. Patel, P. T. Looney, K. C. Young, and M. D. Halling-Brown "Quantitative imaging features: extension of the oncology medical image database", Proc. SPIE 9418, Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, 941812 (17 March 2015); https://doi.org/10.1117/12.2082114

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Databases

Image processing

Medical imaging

Breast

Data modeling

Feature extraction

Oncology

Show All Keywords

Keywords/Phrases

Search In:

Publication Years