“Adrenergic receptors and monoamines in the brain and pancreatic islets of streptozotoein diabetic rats: Role in insulin secretion as a function of age”

I) To study the changes in the content of brain rrrorroamirres in streptozotocirr-irrduced tliabetes as a lirnction of age and to lirrd the role oliadrenal lrornroncs in diabetic state. 2) To assess the adrenergic receptor function in the brain stem ofstreptozotocin-induced diabetic rats ofdillerent ages. 3) To study the changes in the basal levels of second messenger cAMP in the brain stenr ofstreptozotocin-induced diabetic rats as a function of age. 4) To study the changes occurring in the content ofmorroamines and their metabolites in whole pancreas and isolated pancreatic islets of streptozotocin-diabetic rats as a function ofage and the effect of adrenal hormones. 5) To study the adrenergic receptors and basal levels of cAMP in isolated pancreatic islets in young and old streptozotoein-diabetic rats. 6) The in virro study of CAMP content in pancreatic islets of young and old rats and its ellect on glucose induced insulin secretion. 7) 'lhe in vitro study on the involvement of dopamine and corticosteroids in glucose induced insulin secretion in pancreatic islets as a function of age.

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.

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

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.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.

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.

Adrenergic and muscarinic receptor subtypes functional regulation during diabetogenesis: Effect of curcumin and vitamin D3 pre-treatment

In the present study, the initial phase was directed to confirm the effects of curcumin and vitamin D3 in preventing or delaying diabetes onset by studying the blood glucose and insulin levels in the pre-treated and diabetic groups. Behavioural studies were conducted to evaluate the cognitive and motor function in experimental rats. The major focus of the study was to understand the cellular and neuronal mechanisms that ensure the prophylactic capability of curcumin and vitamin D3. To elucidate the mechanisms involved in conferring the antidiabetogenesis effect, we examined the DNA and protein profiles using radioactive incorporation studies for DNA synthesis, DNA methylation and protein synthesis. Furthermore the gene expression studies of Akt-1, Pax, Pdx-1, Neuro D1, insulin like growth factor-1 and NF-κB were done to monitor pancreatic beta cell proliferation and differentiation. The antioxidant and antiapoptotic actions of curcumin and vitamin D3 were examined by studying the expression of antioxidant enzymes - SOD and GPx, and apoptotic mediators like Bax, caspase 3, caspase 8 and TNF-α. In order to understand the signalling pathways involved in curcumin and vitamin D3 action, the second messengers, cAMP, cGMP and IP3 were studied along with the expression of vitamin D receptor in the pancreas. The neuronal regulation of pancreatic beta cell maintenance, proliferation and insulin release was studied by assessing the adrenergic and muscarinic receptor functional regulation in the pancreas, brain stem, hippocampus and hypothalamus. The receptor number and binding affinity of total muscarinic, muscarinic M1, muscarinic M3, total adrenergic, α adrenergic and β adrenergic receptor subtypes were studied in pancreas, brain stem and hippocampus of experimental rats. The mRNA expression of muscarinic and adrenergic receptor subtypes were determined using Real Time PCR. Immunohistochemistry studies using confocal microscope were carried out to confirm receptor density and gene expression results. Cell signalling alterations in the pancreas and brain regions associated with diabetogenesis and antidiabetogenesis were assessed by examining the gene expression profiles of vitamin D receptor, CREB, phospholipase C, insulin receptor and GLUT. This study will establish the anti-diabetogenesis activity of curcumin and vitamin D3 pre-treatment and will attempt to understand the cellular, molecular and neuronal control mechanism in the onset of diabetes.Administration of MLD-STZ to curcumin and vitamin D3 pre-treated rats induced only an incidental prediabetic condition. Curcumin and vitamin D3 pretreated groups injected with MLD-STZ exhibited improved circulating insulin levels and behavioural responses when compared to MLD-STZ induced diabetic group. Activation of beta cell compensatory response induces an increase in pancreatic insulin output and beta cell mass expansion in the pre-treated group. Cell signalling proteins that regulate pancreatic beta cell survival, insulin release, proliferation and differentiation showed a significant increase in curcumin and vitamin D3 pre-treated rats. Marked decline in α2 adrenergic receptor function in pancreas helps to relent sympathetic inhibition of insulin release. Neuronal stimulation of hyperglycemia induced beta cell compensatory response is mediated by escalated signalling through β adrenergic, muscarinic M1 and M3 receptors. Pre-treatment mediated functional regulation of adrenergic and cholinergic receptors, key cell signalling proteins and second messengers improves pancreatic glucose sensing, insulin gene expression, insulin secretion, cell survival and beta cell mass expansion in pancreas. Curcumin and vitamin D3 pre-treatment induced modulation of adrenergic and cholinergic signalling in brain stem, hippocampus and hypothalamus promotes insulin secretion, beta cell compensatory response, insulin sensitivity and energy balance to resist diabetogenesis. Pre-treatment improved second messenger levels and the gene expression of intracellular signalling molecules in brain stem, hippocampus and hypothalamus, to retain a functional neuronal response to hyperglycemia. Curcumin and vitamin D3 protect pancreas and brain regions from oxidative stress by their indigenous antioxidant properties and by their ability to stimulate cellular free radical defence system. The present study demonstrates the role of adrenergic and muscarinic receptor subtypes functional regulation in curcumin and vitamin D3 mediated anti-diabetogenesis. This will have immense clinical significance in developing effective strategies to delay or prevent the onset of diabetes.