Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

EPDM/CIIR Blends : Effect of EPDM Grade on Mechanical Properties

Blends of chlorobutyl rubber (CIIR) with two grades of ethylene-propylene diene monomer rubber (EPDM) were prepared and the effect of blend ratio on the cure characteristics, hot air ageing resistance, steam ageing resistance, and mechanical properties were evaluated. The blend of CIIR with EPDM grade 301 T showed additive behavior and the blend with the other grade of EPDM (NDR 4640) showed synergistic behavior.

HEAVY METAL TOXICITY IN BIVALVE- HISTOLOGICAL AND HISTOCHEMICAL ENQUIRY

Division of Marine Biology, Microbiology and Biochemistry, School of Marine Sciences, Cochin University of Science and Technology

Histological, Histochemical and Biochemical characterisation of male morphotypes of Macrobrachium rosenbergii (de Man)

This thesis entitled “Histological ,Histochemical and biochemical characterisation of male morphotypes of macrobrachium rosenbergii(De Man). The giant fresh water prawn Macrobrachium rosenbergii (de Man) is emerging as a prime candidate species in fresh water aquaculture as a global basis and therefore, receiving much attention in recent years.the present work was aimed at to study the histological variations, if any, in the reproductive system viz. testes, vas deferens including androgenic gland, hepatopancreas and the neurosecretory system viz. eye stalk, brain and thoracic ganglion among the male morphotypes and their transitional stages of M.rosenbergii from growouts. This study was also aimed at to bring out the histochemical variations, if any, in the reproductive system comprising of testes, vas deferens including androgenic gland and the hepatopancreas among the male morphotypes and their transitional stages collected from growouts. Biochemical characterisation of various male morphotyes and their transitional stages have also been attempted in order to find out biochemical evidence, if any, in the morphotypic transformation.Histological study of the testes of three male morphotypes viz., SM, SOC and SBC and their 'transitional stages viz., WOC, tSOC, WBC and OBC have been carried out with a view to unravel the structural and functional differences of the testes, if any, of these morphotypes. Studies on the lipid components viz., cholesterol, phospholipid and triglyceride In the muscle tissue and hepatopancreas have been carried out.

Studies on the histological and biochemical changes during spermatogenesis in MUGIL CEPHALUS linnaeus and related species

Several studies on the biology and fisheries of mullets, particularly of M cephalus are now available. Different aspects of breeding, larval rearing, seed production, field culture and ecophysiology have also been investigated. However, information on the spermatogenesis in M cephalus as well as L parsia is scanty. Since an understanding of the reproductive strategies is an essential pre-requisite for evolving successful breeding programmes through artificial fertilization and gametic preservation, investigations on spermatogenesis in these species were taken up and the results are presented in this thesis. The thesis is presented in 9 chapters

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.