Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Application of ocean colour data to study the oceanographic and atmospheric features off the southwest coast of India with special reference to upwelling

Satellite remote sensing is being effectively used in monitoring the ocean surface and its overlying atmosphere. Technical growth in the field of satellite sensors has made satellite measurement an inevitable part of oceanographic and atmospheric research. Among the ocean observing sensors, ocean colour sensors make use of visible band of electromagnetic spectrum (shorter wavelength). The use of shorter wavelength ensures fine spatial resolution of these parameters to depict oceanographic and atmospheric characteristics of any region having significant spaio-temporal variability. Off the southwest coast of India is such an area showing very significant spatio-temporal oceanographic and atmospheric variability due to the seasonally reversing surface winds and currents. Consequently, the region is enriched with features like upwelling, sinking, eddies, fronts, etc. Among them, upwelling brings nutrient-rich waters from subsurface layers to surface layers. During this process primary production enhances, which is measured in ocean colour sensors as high values of Chl a. Vertical attenuation depth of incident solar radiation (Kd) and Aerosol Optical Depth (AOD) are another two parameters provided by ocean colour sensors. Kd is also susceptible to undergo significant seasonal variability due to the changes in the content of Chl a in the water column. Moreover, Kd is affected by sediment transport in the upper layers as the region experiences land drainage resulting from copious rainfall. The wide range of variability of wind speed and direction may also influence the aerosol source / transport and consequently AOD. The present doctoral thesis concentrates on the utility of Chl a, Kd and AODprovided by satellite ocean colour sensors to understand oceanographic and atmospheric variability off the southwest coast of India. The thesis is divided into six Chapters with further subdivisions

A structural investigation relating to the pozzolanic activity of rice husk ashes

Various factors determine the applicability of rice husk ash (RHA) as a pozzolanic material. The amount and accessibility of reactive sites is thought to be a key factor. A structural study of RHA samples in relation to their reactivity has been performed; Silica in RHA formed by burning rice husk in a laboratory furnace under continuous supply of air have been characterized as a function of incineration temperature, time and cooling regime. The characterization methods included chemical analyses, conductivity measurements, microscopic analysis, X-ray diffraction (XRD) and 29Si magic-angle spinning (MAS) nuclear magnetic resonance (NMR). In line with earlier observations, the analyses show that the highest amounts of amorphous silica occur in samples burnt in the range of 500 °C–700 °C. The 29Si NMR data allow direct identification of the reactive silanol sites in the RHA samples. De-convolution of the NMR spectra clearly shows that the quickly cooled RHA resulting from burning rice husk for 12 h at 500 °C has the highest amount of silanol groups. This sample also induced the largest drop in conductivity when added to a saturated calcium hydroxide solution giving an indication of its reactivity towards lime. Therefore, this RHA is the favorable sample to be used as pozzolanic cement additive