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Abstract:
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Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging
technique in which stacks of images are acquired with different tissue
contrasts. Simultaneous observation and quantitative analysis of normal brain
tissues and small abnormalities from these large numbers of different
sequences is a great challenge in clinical applications. Multispectral MRI
analysis can simplify the job considerably by combining unlimited number of
available co-registered sequences in a single suite. However, poor
performance of the multispectral system with conventional image
classification and segmentation methods makes it inappropriate for clinical
analysis. Recent works in multispectral brain MRI analysis attempted to
resolve this issue by improved feature extraction approaches, such as
transform based methods, fuzzy approaches, algebraic techniques and so
forth. Transform based feature extraction methods like Independent
Component Analysis (ICA) and its extensions have been effectively used in
recent studies to improve the performance of multispectral brain MRI
analysis. However, these global transforms were found to be inefficient and
inconsistent in identifying less frequently occurred features like small lesions,
from large amount of MR data.
The present thesis focuses on the improvement in ICA based feature
extraction techniques to enhance the performance of multispectral brain MRI
analysis. Methods using spectral clustering and wavelet transforms are
proposed to resolve the inefficiency of ICA in identifying small
abnormalities, and problems due to ICA over-completeness. Effectiveness of
the new methods in brain tissue classification and segmentation is confirmed
by a detailed quantitative and qualitative analysis with synthetic and clinical,
normal and abnormal, data. In comparison to conventional classification
techniques, proposed algorithms provide better performance in classification
of normal brain tissues and significant small abnormalities. |