PCA based spatio-temporal decomposition and compression of 4D medical images

Cristian Lorenz; Jens von Berg

doi:10.1117/12.595257

15 April 2005 PCA based spatio-temporal decomposition and compression of 4D medical images

Cristian Lorenz, Jens von Berg

Proceedings Volume 5748, Medical Imaging 2005: PACS and Imaging Informatics; (2005) https://doi.org/10.1117/12.595257
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

The amount of per-patient image data generated by medical imaging modalities such as MRI and multi-slice CT scanners increases rapidly. This is on one hand due to an increasing spatial image resolution and on the other hand due to the expanding use of multi-phase or cine studies. A cardiac multi-phase CT scan, generated in about 20 seconds scan time, easily generates about 2GB of image data. The visualization and further processing of those data is at the edge of the abilities of current computers. We therefore present a principal component analysis (PCA) based compression algorithm, which exploits the spatial and temporal coherence of medical multi-phase image data and which allows to retrieve the images with a selectable amount of information loss. The main focus of this work is to reduce the required amount of system memory, not to reduce the required amount of disk space, i.e. at any time only the decomposed image resides in the system memory. If an intensity value for a position (x,y,z,t) is required, it is calculated on demand. This is possible, since the intensity values are expressed as fast computable weighted sums. The method has been applied to cardiac multi-phase CT datasets. It could be shown that a compression ratio of 3:1 still keeps the compression-induced losses (mainly blurring) at the noise level of the original data (about 5 Hounsfield units). Compression ratios of 5:1 and more can be achieved keeping an undisturbed visual impression of the dataset. The influence of the image compression on an automated cardiac segmentation procedure has been studied. Compression ratios up to 8:1 lead to results that only marginally deviate from results of the uncompressed image.

Citation Download Citation

Cristian Lorenz and Jens von Berg "PCA based spatio-temporal decomposition and compression of 4D medical images", Proc. SPIE 5748, Medical Imaging 2005: PACS and Imaging Informatics, (15 April 2005); https://doi.org/10.1117/12.595257

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
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image compression

Medical imaging

Visualization

Principal component analysis

Image processing

Image segmentation

Computed tomography

Show All Keywords

Keywords/Phrases

Search In:

Publication Years