Efficient reconfigurable architectures for 3-D medical image compression
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...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3654/1/24p%20AFANDI%20AHMAD.pdf http://eprints.uthm.edu.my/3654/ |
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| Summary: | 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. |
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