Fractal image compression with quadtrees
Fractal image compression
A Novel Approach to Computer-Aided Diagnosis of Mammographic Images
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Evaluating quality and utility in digital mammography
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Mammogram compression using super-resolution
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
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The concept of content-based image compression (CBIC) has far reaching effects in the areas of archiving and telecommunications. The purpose of this paper is to present some pilot study results from the application of CBIC to mammography. Unlike traditional compression approaches, CBIC first analyzes the content of the data before compression takes place. In this approach, prior to compression, the data is preprocessed and is segmented into two non-overlapping regions: (1) focus-of-attention regions (FARs) that contain the 驴important驴 segments of the data, and (2) background regions. Subsequently, the former regions are compressed using a lossless compression technique (maintaining fidelity), while the latter regions are compressed with the aid of a lossy technique (attaining large reductions in data). The intended result is an optimal balance between data reduction and data fidelity. In this case, compression ratios 5-6 times greater than that of lossless compression alone can be reached while preserving the important information.