Efficient reconfigurable architectures for 3-D medical image compression

• Main Author: Ahmad, Afandi
• Format: Thesis
• Language: English
• Published: 2010
• Subjects: TA Engineering (General). Civil engineering (General); TA1501-1820 Applied optics. Photonics
• Online Access: http://eprints.uthm.edu.my/3654/1/24p%20AFANDI%20AHMAD.pdf; http://eprints.uthm.edu.my/3654/
• Institution: Universiti Tun Hussein Onn Malaysia

Abstract Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these �elds, medical image compression is important since both e�cient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain e�cient solutions for large medical volumes data, an e�cient implementation of these operations is of signi�cant importance. Recon�gurable hardware, in the form of �eld programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial recon�guration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial recon�guration (DPR) has been introduced. Comparative study for both non-partial and partial recon�guration implementation has shown that DPR o�ers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the �nite Radon transform (FRAT) with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-o�s on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is signi�cantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-de�nition (HD) images in real-time without any bu�er between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a signi�cant achievement in terms of the architecture e�ciency and applications performance.