Mammogram compression using adaptive prediction

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


The JPEG lossless compression technique uses pixel value prediction based on the nearest neighbor pixel values. Usually a single predictor is used for the entire image. Recent work has shown that better compression performance can be achieved by choosing the predictors adaptively depending on the context of surrounding pixel or predictor values. This method is computationally lengthy and memory intensive. In mammograms the image contents can be separated into three distinct visual classes: background, smooth and textured, corresponding to three classes of predictors available in JPEG. This paper discusses an approach to exploiting the use of these classes directly for predictor choice.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsYongmin Kim
Number of pages8
Publication statusPublished - 27 Apr 1995
Externally publishedYes
EventMedical Imaging 1995: Image Display - San Diego, CA, USA
Duration: 26 Feb 199528 Feb 1995

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceMedical Imaging 1995: Image Display
CitySan Diego, CA, USA


Dive into the research topics of 'Mammogram compression using adaptive prediction'. Together they form a unique fingerprint.

Cite this