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Preclinical micro-computed tomography (microCT) images are of utility for 3D morphological bone evaluation, which is of great interest in cancer detection and treatment development. This work introduces a compression strategy for microCTs that allocates specific substances in different Volumes of Interest (Vols). The allocation procedure is conducted by the Hounsfield scale. The Vols are coded independently and then grouped in a single DICOM-compliant file. The proposed method permits the use of different codecs, identifies and transmit data corresponding to a particular substance in the compressed domain without decoding the volume(s), and allows the computation of the 3D morphometry without needing to store or transmit the whole image. The proposed approach reduces the transmitted data in more than 90% when the 3D morphometry evaluation is performed in high density and low density bone. This work can be easily extended to other imaging modalities and applications that work with the Hounsfield scale. (C) 2015 Elsevier Ltd. All rights reserved.

A contextual lightweight arithmetic coder is proposed for lossless compression of medical imagery. Context definition uses causal data from previous symbols coded, an inexpensive yet efficient approach. To further reduce the computational cost, a binary arithmetic coder with fixed-length codewords is adopted, thus avoiding the normalization procedure common in most implementations, and the probability of each context is estimated through bitwise operations. Experimental results are provided for several medical images and compared against state-of-the-art coding techniques, yielding on average improvements between nearly 0.1 and 0.2 bps.

Our society increasingly relies on the transmission and reception of vast amounts of data using serial connections featuring ever-increasing bit rates. In imaging systems, for example, the frame rate achievable is often limited by the serial link between camera and host even when modern serial buses with the highest bit rates are used. This thesis documents a scalable embedded transceiver system with a bandwidth and interface standard that can be adapted to suit a particular application. This new approach for a real-time scalable embedded transceiver system is referred to as a Novel Reference Model (NRM), which connects two or more applications through a transceiver network in order to provide real-time data to a host system. Different transceiver interfaces for which the NRM model has been tested include: LVDS, GIGE, PMA-direct, Rapid-IO and XAUI, one support a specific range for transceiver speed that suites a special type for transceiver physical medium. The scalable serial link approach has been extended with loss-less data compression with the aim of further increasing dataflow at a given bit rate. Two lossless compression methods were implemented, based on Huffman coding and a novel method called Reduced Lossless Compression Method (RLCM). Both methods are integrated into the scalable transceivers providing a comprehensive solution for optimal data transmission over a variety of different interfaces. The NRM is implemented on a field programmable gate array (FPGA) using a system architecture that consists of three layers: application, transport and physical. A Terasic DE4 board was used as the main platform for implementing and testing the embedded system, while Quartus-II software and tools were used to design and debug the embedded hardware systems.