“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.

Systematics and pathogenicity of vibrionaceae associated with the larvae of macrobrachium rosenbergii in hatchery

The great potential for the culture of non-penaeid prawns, especially Macrobrachium rosenbergii in brackish and low saline areas of Indian coastal zone has not yet been fully exploited due to the non availability of healthy seed in adequate numbers and that too in the appropriate period. In spite of setting up several prawn hatcheries around the country to satiate the ever growing demands for the seed of the giant fresh water prawn, the supply still remains fear below the requirement mainly due to the mortality of the larvae at different stages of the larval cycle. In a larval rearing system of Macrobrachium rosenbergii, members of the family Vibrionaceae were found to be dominant flora and this was especially pronounced during the times of mortality However, to develop any sort of prophylactic and therapeutic measures, the pathogenic strains have to be segregated from the lot. This would never be possible unless they were clustered based on the principles of numerical taxonomy It is with these objectives and requirements that the present work involving phenotypic characterization of the isolates belonging to the family Vibrionaceae and working out the numerical taxonomy, determination of mole % G+C ratio, segregation of the pathogenic strains and screening antibiotics as therapeutics at times of emergency, was carried out.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING

Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier using different approaches to feature selection and tokenization on different email corpora

Probiotic effect of Bacillus NL110 and Vibrio NE17 on the survival, growth performance and immune response of Macrobrachium rosenbergii (de Man)

Eight hundred and eighty-¢ve strains of bacterial isolates fromvarious samples associatedwith the natural habitat ofMacrobrachiumrosenbergii were screened for their probiotic potential. Two putative probionts namely Bacillus NL110 and Vibrio NE17 isolated from the larvae and egg samples, respectively, were selected for experimental studies and were introduced to the juveniles of M. rosenbergii (0.080 0.001g) through di¡erent modes such as through feed, water and both. The probiotic potential of the above bacteria in terms of improvements inwater quality, growth, survival, speci¢c growth rate (SGR), feed conversion ratio and immune parameters was evaluated. The treatment groups showed a signi¢cant improvement in SGR and weight gain (Po0.001). Survival among di¡erent treatment groups was better than that in the control group. There were also signi¢cant improvements in the water quality parameters such as the concentration of nitrate and ammonia in the treatment groups (Po0.05). Improvements in immune parameters such as the total haemocyte count (Po0.05), phenoloxidase activity and respiratory burst were also signi¢cant (Po0.001). It is concluded that screening of the natural micro£ora of cultured ¢sh and shell¢sh for putative probionts might yield probiotic strains of bacteria that could be utilized for an environment-friendly and organic mode of aquaculture.

Underwater Target Localization, Tracking and Classification

Underwater target localization and tracking attracts tremendous research interest due to various impediments to the estimation task caused by the noisy ocean environment. This thesis envisages the implementation of a prototype automated system for underwater target localization, tracking and classification using passive listening buoy systems and target identification techniques. An autonomous three buoy system has been developed and field trials have been conducted successfully. Inaccuracies in the localization results, due to changes in the environmental parameters, measurement errors and theoretical approximations are refined using the Kalman filter approach. Simulation studies have been conducted for the tracking of targets with different scenarios even under maneuvering situations. This system can as well be used for classifying the unknown targets by extracting the features of the noise emanations from the targets.

Probiotic effect of Bacillus NL110 and Vibrio NE17 on the survival, growth performance and immune response of Macrobrachium rosenbergii (de Man)

Eight hundred and eighty-¢ve strains of bacterial isolates fromvarious samples associatedwith the natural habitat ofMacrobrachiumrosenbergii were screened for their probiotic potential. Two putative probionts namely Bacillus NL110 and Vibrio NE17 isolated from the larvae and egg samples, respectively, were selected for experimental studies and were introduced to the juveniles of M. rosenbergii (0.080 0.001g) through di¡erent modes such as through feed, water and both. The probiotic potential of the above bacteria in terms of improvements inwater quality, growth, survival, speci¢c growth rate (SGR), feed conversion ratio and immune parameters was evaluated. The treatment groups showed a signi¢cant improvement in SGR and weight gain (Po0.001). Survival among di¡erent treatment groups was better than that in the control group. There were also signi¢cant improvements in the water quality parameters such as the concentration of nitrate and ammonia in the treatment groups (Po0.05). Improvements in immune parameters such as the total haemocyte count (Po0.05), phenoloxidase activity and respiratory burst were also signi¢cant (Po0.001). It is concluded that screening of the natural micro£ora of cultured ¢sh and shell¢sh for putative probionts might yield probiotic strains of bacteria that could be utilized for an environment-friendly and organic mode of aquaculture

Application of bacterins and yeast Acremonium dyosporii to protect the larvae of Macrobrachium rosenbergii from vibriosis

The fresh water prawn, Macrobrachium rosenbergii, has proven potential for use as an aquaculture species (Hanson & Goodwin, 1997; Kurup, 1984). In India alone, culture of this species of prawn in low saline areas requires about 200 million seed per year (Kurup, 1984). In hatcheries poor survival rate has been associated with vibriosis at di#erent stages of the larval cycle. Members of the family Vibrionaceae associated with the larvae of M. rosenbergii were shown to be pathogenic under laboratory conditions (Bhat et al., 2000, in press). Vibrios have been associated with mortality of penaeid prawns by several workers (Aquacop, 1977; Hameed, 1993; Karunasagar et al., 1994). Two methods have been suggested to protect both the larvae and juveniles from vibriosis; one is the administration of bacterins prepared from pathogenic strains (Itami et al., 1989, 1991; Adams, 1991; Song & Sung, 1990; Sung et al., 1991) and the other is the utilization of yeast 1-3 and 1-6 glucans as immunostimulants for enhancing the non-specific defense system (Sung et al., 1994; Song et al., 1997). In the light of these observations it was hypothesised that bacterins and yeast glucans may also be e#ective in protecting the larvae of M. rosenbergii from vibriosis as has been achieved in the case of penaeids. To examine this hypothesis, the ability of bacterins and an extracellular glucan-producing yeast to increase the overall survival and metamorphosis of larvae in a hatchery, as well as to protect against an experimental challenge under laboratory conditions, was evaluated

Isolation of a pathogenic strain of Vibrio alginolyticus from necrotic larvae of Macrobrachium rosenbergii (de Man)

Giant freshwater prawn, Macrobrachium rosenbergii (de Man), is an important commercial species with considerable export value, ideal for cultivation under low saline conditions and in freshwater zones (Kurup 1994). However, despite more than a decade of research on its larval production systems, vibriosis still hampers seed production resulting in high mortality rates. Among the different species of vibrios, Vibrio alginolyticus has been isolated frequently from diseased shrimp as the aetiological agent of vibriosis and has been described as a principal pathogen of both penaeids and nonpenaeids (Lightner 1988; Baticados, Cruz-Lacierda, de la Cruz, Duremdez-Fernandez, Gacutan, Lavilla- Pitogo & Lio-Po 1990; Mohney, Lightner & Bell 1994; Lee, Yu, Chen, Yang & Liu 1996). Vibrio fluvialis, V. alginolyticus, V. cholerae non-O1 (Fujioka & Greco 1984), Aeromonas liquifaciens and V. anguillarum (Colorni 1985) have been isolated from the larvae of M. rosenbergii. A profound relationship between the abundance of members of the family Vibrionaceae and larval mortality (Singh 1990) and the predominance of Vibrio in eggs, larvae and post-larvae of M. rosenbergii (Hameed, Rahaman, Alagan & Yoganandhan 2003) was reported. The present paper reports the isolation, characterization, pathogenicity and antibiotic sensitivity of V. alginolyticus associated with M. rosenbergii larvae during an occurrence of severe mass mortality at the ninth larval stage.

Vibrios associated with Macrobrachium rosenbergii (De Man, 1879) larvae from three hatcheries on the Indian southwest coast

Surveys for bacteriological analysis of larval samples to isolate the associated vibrios were carried out during 1985^1992, 2001 and 2002 in three di¡erent hatcheries located on the southwest coast of India. Vibrio isolates were examined for their species diversity, virulence based on haemolysis in prawn blood agar, lipolysis, proteolysis and chitinolysis and antibiotic sensitivity.Vibrio cholerae was the predominant species in the apparently healthy larval samples, whereas V. alginolyticus and V. vulni¢cus dominated during disease and morbidity. No correlation was found between the hydrolytic properties and haemolytic activity of the vibrios associated with the larvae. All isolates were resistant to erythromycin and resistance to oxytetracycline, ampicillin and streptomycin sulphate was prevalent among the larger section of the Vibrio population. This suggested that antibiotic application may not be of much use to protect the larvae fromvibriosis. This is the ¢rst report on the diversity of Vibrio species associated with Macrobrachium rosenbergii larvae and their virulence characteristics based on haemolysis in prawn blood agar

Antimicrobial activity of chitosan against vibrios from freshwater prawn Macrobrachium rosenbergii larval rearing systems

Chitosan is a biocompatible and biodegradable natural polymer with established antimicrobial properties against specific microorganisms. The present study demonstrates its antibacterial activity against 48 isolates of Vibrio species from prawn larval rearing systems. The antibacterial activity had a positive correlation with the concentration of chitosan. This work opens up avenues for using chitosan as a prophylactic biopolymer for protecting prawn larvae from vibriosis.

A simple device for the separation of weak larvae of Macrobrachium rosenbergii (De Man)

Larvae of Macrobrachium rosenbergii (De Man) are photopositive (Ling 1969a.b) and negatively rheotactic. While investigating larval diseases of M, rosenbergii it was observed that weak larvae failed to show both these responses. It was felt that this lack of response could be used to develop a device for separating the weak larvae from the apparently healthy ones. Such a device would be a valuable tool for assessing the health of a batch in terms of the percentage of 'healthy' and 'weak' larvae. What follows is a description and mode of operation of the 'photo-flow' device developed by the authors

Chitosan as a wall material for a microencapsulated delivery system for Macrobrachium rosenbergii(de Man) larvae

Chitosan has beenwidely accepted as awall material for preparing microcapsules of various purposes in human medicine. The possibility of using chitosan as a wall material for microencapsulating nutrients and drugs for aquaculture purposes, speci¢cally to Macrobrachium rosenbergii larvae was evaluated in this study. Two types of chitosan-coated microcapsules were prepared using either acetone (MEC-A) or NaOH (MEC-N) as the cross-linking agents. They were compared with a microbound diet relative to total leaching of nutrients and free amino acids (FAA). Among the microcapsules, MEC-N showed the lowest level of total leaching of nutrients (23.3%) during 5 h of immersion in seawater and released 65% FAA after 60min. During laboratory trials,75% larvae had accepted the MEC-N capsule. The results of the study suggest that chitosan can be used as a wall material for preparing microcapsules to deliver drugs and nutrients to M. rosenbergii larvae.

A Classification of Sandhi Rules for Suffix Separation in Malayalam

Suffix separation plays a vital role in improving the quality of training in the Statistical Machine Translation from English into Malayalam. The morphological richness and the agglutinative nature of Malayalam make it necessary to retrieve the root word from its inflected form in the training process. The suffix separation process accomplishes this task by scrutinizing the Malayalam words and by applying sandhi rules. In this paper, various handcrafted rules designed for the suffix separation process in the English Malayalam SMT are presented. A classification of these rules is done based on the Malayalam syllable preceding the suffix in the inflected form of the word (check_letter). The suffixes beginning with the vowel sounds like ആല, ഉെെ, ഇല etc are mainly considered in this process. By examining the check_letter in a word, the suffix separation rules can be directly applied to extract the root words. The quick look up table provided in this paper can be used as a guideline in implementing suffix separation in Malayalam language

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified