“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

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.

A simple linear magnetic field sweep generator for magnetic resonance experiments

A simple and inexpensive linear magnetic field sweep generating system suitable for magnetic resonance experiments is described. The circuit, utilising a modified IC bootstrap configuration, generates field sweep over a wide range of sweep durations with excellent sweep linearity.

Swift Heavy Ion Irradiation and Thermal Annealing Induced Modification of Structural, Topographical and Magnetic Properties in Monolayer and Bilayer Films Based on FeNiMoB and Zinc Ferrite

Magnetism and magnetic materials have been playing a lead role in the day to day life of human beings. The human kind owes its gratitude to the ‘lodestone’ meaning ‘leading stone’ which lead to the discovery of nations and the onset of modern civilizations. If it was William Gilbert, who first stated that ‘earth was a giant magnet’, then it was the turn of Faraday who correlated electricity and magnetism. Magnetic materials find innumerable applications in the form of inductors, read and write heads, motors, storage devices, magnetic resonance imaging and fusion reactors. Now the industry of magnetic materials has almost surpassed the semiconductor industry and this speaks volumes about its importance. Extensive research is being carried out by scientists and engineers to remove obsolescence and invent new devices. Though magnetism can be categorized based on the response of an applied magnetic field in to diamagnetic, paramagnetic, ferromagnetic, ferrimagnetic and antiferromagnetic; it is ferrimagnetic, ferromagnetic and antiferromagnetic materials which have potential applications. The present thesis focusses on these materials, their composite structures and different ways and means to modify their properties for useful applications.