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.

DETECTION OF BILIARY FUNCTIONAL PROBLEMS USING MICROWAVES

This article reports a new in vitro bile analysis based on the measurement of the dielectric properties at microwave frequencies. The measurements were made using rectangular cavity perturbation technique at the S-band of microwave frequency with the different samples of bile obtained from healthy persons as well as from patients. It is observed that an appreciable change in the dielectric properties of patient’s samples with the normal healthy samples and these measurements were in good agreement with clinical analysis. These results prove an alternative in-vitro method of detecting bile abnormalities based on the measurement of the dielectric properties of bile samples using microwaves without surgical procedure.

HUMAN PERICARDIAL FLUID AT MICROWAVE FREQUENCIES

This article reports a new method of analyzing pericardial fluid based on the measurement of the dielectric properties at microwave frequencies. The microwave measurements were performed by rectangular cavity perturbation method in the S-band of microwave frequency with the pericardial fluid from healthy persons as well as from patients suffering from pericardial effusion. It is observed that a remarkable change in the dielectric properties of patient samples with the normal healthy samples and these measurements were in good agreement with clinical analysis. This measurement technique and the method of extraction of pericardial fluid are simple. These results give light to an alternative in-vitro method of diagnosing onset pericardial effusion abnormalities using microwaves without surgical procedure.

“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.

Novel Electrochemical and Fluorescence Sensors for Food Additives and Neurotransmitters

There is an enormous demand for chemical sensors in many areas and disciplines including chemistry, biology, clinical analysis, environmental science. Chemical sensing refers to the continuous monitoring of the presence of chemical species and is a rapidly developing field of science and technology. They are analytical devices which transform chemical information generating from a reaction of the analyte into an measurable signal. Due to their high selectivity, sensitivity, fast response and low cost, electrochemical and fluorescent sensors have attracted great interest among the researchers in various fields. Development of four electrochemical sensors and three fluorescent sensors for food additives and neurotransmitters are presented in the thesis. Based on the excellent properties of multi walled carbon nanotube (MWCNT), poly (L-cysteine) and gold nanoparticles (AuNP) four voltammetric sensors were developed for various food additives like propyl gallate, allura red and sunset yellow. Nanosized fluorescent probes including gold nanoclusters (AuNCs) and CdS quantum dots (QDs) were used for the fluorescent sensing of butylated hydroxyanisole, dopamine and norepinephrine. A total of seven sensors including four electrochemical sensors and three fluorescence sensors have been developed for food additives and neurotransmitters.

Development of Electrochemical and Fluorescent Sensors

Chemical sensors have growing interest in the determination of food additives, which are creating toxicity and may cause serious health concern, drugs and metal ions. A chemical sensor can be defined as a device that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytically useful signal. The chemical information may be generated from a chemical reaction of the analyte or from a physical property of the system investigated. Two main steps involved in the functioning of a chemical sensor are recognition and transduction. Chemical sensors employ specific transduction techniques to yield analyte information. The most widely used techniques employed in chemical sensors are optical absorption, luminescence, redox potential etc. According to the operating principle of the transducer, chemical sensors may be classified as electrochemical sensors, optical sensors, mass sensitive sensors, heat sensitive sensors etc. Electrochemical sensors are devices that transform the effect of the electrochemical interaction between analyte and electrode into a useful signal. They are very widespread as they use simple instrumentation, very good sensitivity with wide linear concentration ranges, rapid analysis time and simultaneous determination of several analytes. These include voltammetric, potentiometric and amperometric sensors. Fluorescence sensing of chemical and biochemical analytes is an active area of research. Any phenomenon that results in a change of fluorescence intensity, anisotropy or lifetime can be used for sensing. The fluorophores are mixed with the analyte solution and excited at its corresponding wavelength. The change in fluorescence intensity (enhancement or quenching) is directly related to the concentration of the analyte. Fluorescence quenching refers to any process that decreases the fluorescence intensity of a sample. A variety of molecular rearrangements, energy transfer, ground-state complex formation and collisional quenching. Generally, fluorescence quenching can occur by two different mechanisms, dynamic quenching and static quenching. The thesis presents the development of voltammetric and fluorescent sensors for the analysis of pharmaceuticals, food additives metal ions. The developed sensors were successfully applied for the determination of analytes in real samples. Chemical sensors have multidisciplinary applications. The development and application of voltammetric and optical sensors continue to be an exciting and expanding area of research in analytical chemistry. The synthesis of biocompatible fluorophores and their use in clinical analysis, and the development of disposable sensors for clinical analysis is still a challenging task. The ability to make sensitive and selective measurements and the requirement of less expensive equipment make electrochemical and fluorescence based sensors attractive.