Virtually lossless compression of medical images through classified prediction and context-based arithmetic coding

Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Franco Lotti

doi:10.1117/12.334609

28 December 1998 Virtually lossless compression of medical images through classified prediction and context-based arithmetic coding

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Franco Lotti

Author Affiliations +

Proceedings Volume 3653, Visual Communications and Image Processing '99; (1998) https://doi.org/10.1117/12.334609
Event: Electronic Imaging '99, 1999, San Jose, CA, United States

Abstract

This paper proposes a method to achieve a virtually-lossless compression of medical images. An image is normalized to the standard deviation of its noise, which is adaptively estimated in an unsupervised fashion. The resulting bit map is encoded without any further loss. The compression algorithm is based on a classified linear-regression prediction followed by context-based arithmetic coding of the outcome residuals. Images are partitioned into blocks, e.g., 16 X 16, and a minimum mean square (MMSE) linear predictor is calculated for each block. Given a preset number of classes, a Fuzzy-C-Means algorithm produces an initial guess of classified predictors to be fed to an iterative procedure which classifies pixel blocks simultaneously refining the associated predictors. All the predictors are transmitted along with the label of each block. Coding time are affordable thanks to fast convergence of the iterative algorithms. Decoding is always performed in real time. The compression scheme provides impressive performances, especially when applied to X-ray images.

Citation Download Citation

Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Franco Lotti "Virtually lossless compression of medical images through classified prediction and context-based arithmetic coding", Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); https://doi.org/10.1117/12.334609

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