“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.

Biostatistical evaluation and Management of sciaenid fishery with particular reference to Otolithes ruber (Schneider, 1801) andJohnieops sina (Cuvier, 1830) of Konkan coast

Fish, a natural resource, has received great attention from all over the world. since it provides a cheap protein, employment and income to the millions of people for centuries. So fishermen, industrialist and multinationals are trying to exploit the marine resources to their maximum benefit by using modern craft, advance fishing equipments and efficient gear. Fishery resources in the open system particularly in oceans, were considered to be unlimited. However, recent developments in the innovation of efficient craft and gear using well tested material fitted with modern equipments that have greatly enhanced the mobility of craft, agility of gear and the ability of equipments to locate fishery resources have proved otherwise. Hence as the exploitation increases with more effort entering the fishery, the catch per unit of fishing effort starts to decline due to the limitness of the resources. The heavy fishing pressure in the recent past led to commercial extinction of a number of stocks such as. the North Sea herring, California sardine, Japanese Sardine and Peruvian anchovy (FAO. 1968: Gulland, 1974). In India, seer fish from Palk-Bay declined due to uncontrolled fishing (Devaraj 1983).