Compression of biomedical images and signals by Amine Nait-Ali, Christine Cavaro-Menard

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By Amine Nait-Ali, Christine Cavaro-Menard

Over the last decade, snapshot and sign compression for garage and transmission goal has obvious a very good growth. yet what approximately scientific information compression? may still a scientific photo or a physiological sign be processed and compressed like several different info? The development made in imaging platforms, storing structures and telemedicine makes compression during this box relatively fascinating. despite the fact that, this compression needs to be tailored to the specificities of biomedical facts which include prognosis info. As such, this booklet deals an outline of compression suggestions utilized to clinical information, together with: physiological signs, MRI, X-ray, ultrasound photographs, static and dynamic volumetric pictures. Researchers, clinicians, engineers and pros during this sector, besides postgraduate scholars within the sign and photograph processing box, will locate this ebook to be of significant curiosity.

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These methods differ in their wavelet coefficient selection procedure, before the quantization stage. Two key approaches enable the elimination of insignificant coefficients: – multiresolution selection, which applies thresholds through the subbands separately in each direction (inter-band technique) [SHA 93], [SAI 96]; – selection in each subband, which consists of setting to zero all the insignificant coefficients below a given threshold (intra-band technique) [TAU 00]. 1. Inter-band techniques Even though wavelet transform generates subbands which correspond to orthogonal projections in disjoint vector subspaces, structural similarities remain in the subbands.

This method was retained as one of the three methods in the JPEG study, along with the Block Truncation Coding approach (another spatial approach coding a block of pixels), and the DCT technique. The latter falls into the second category, which changes the representation space before quantization and coding. The different standards 2D, 2D+t, 3D are based on different orthogonal transform. This does not mean that methods such as DPCM have been abandoned. We find them in the standards, every time we need to reduce redundancy in a neighborhood (for example to code the average value of blocks in JPEG or to predict the spatial position of the vertices in 3D meshes in MPEG4).

In this chapter, we have opted not to detail quantization methods. These methods can be studied in the references [MAX 60], [LIN 80] and [GRA 84]. However, when we detail the norms, the quantization stage will also be looked at in further detail. 3. Coding the quantized values The values which emerge from the quantization process are generally represented by a binary code of fixed length (N bits/symbol). The coding stage allows us to reduce the average number of bits allocated to each quantized value.

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