Medical image compression with lossless regions of interest
Signal Processing
Future Generation Computer Systems - Special issue on ITIS—an international telemedical information society
Region-adaptive transform based on a stochastic model
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Efficiency of shape-adaptive 2-D transforms for coding of arbitrarily shaped image segments
IEEE Transactions on Circuits and Systems for Video Technology
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Compression of medical images (MI) is an important field of study in biomedical engineering. Although lossy compression could solve storage space and transmission bandwidth problems, it is not recommended by most physicians because of data loss. Lossless compression saves all details inside image; however it could not be applied for the whole image area. This paper introduces a hybrid lossless compression channel with two different steps. The first is an automatic segmentation technique, where a region of interest (ROI) is automatically segmented by aid of an artificial neural network (ANN) and an introduced difference fuzzy model (IDFM). The second is a modified arithmetic coding (MAC) lossless compression algorithm. This hybrid channel is to combine in parallel with a lossy compression channel that transmits non important parts of the MI progressively, using the fast algorithm of embedded zerotree wavelet (FEZW) [1]. The proposed technique reduces complexity, storage space, bandwidth, and saves time. Moreover, it is a fully automatic system. Several brain magnetic resonance images (MRI) and fluorescene ophthalmic images are analyzed.