An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Single Frame Image Super Resolution Using Learned Directionlets

In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

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.

Decreased 5-HT2C receptor binding in the cerebral cortex and brain stem during pancreatic regeneration in rats

The purpose of this study was to investigate the role of central 5-HT2C receptor binding in rat model of pancreatic regeneration using 60-70% pancreatectomy. The 5-HT and 5-HT2c receptor kinetics were studied in cerebral cortex and brain stem of sham operated, 72 h pancreatectomised and 7 days pancreatectomised rats. Scatchard analysis with [3H] mesulergine in cerebral cortex showed a significant decrease (p < 0.05) in maximal binding (B^,ax) without any change in Kd in 72 h pancreatectomised rats compared with sham. The decreased Bmax reversed to sham level by 7 days after pancreatectomy. In brain stem , Scatchard analysis showed a significant decrease (p < 0.01) in Bax with a significant increase (p < 0.01) in Kd. Competition analysis in brain stem showed a shift in affinity towards a low affinity. These parameters were reversed to sham level by 7 days after pancreatectomy. Thus the results suggest that 5-HT through the 5-HT2C receptor in the brain has a functional regulatory role in the pancreatic regeneration. (Mol Cell Biochem 272: 165-170, 2005)

Enhanced /3-adrenergic receptors in the brain and pancreas during pancreatic regeneration in weanling rats

Adrenergic stimulation has an inyortant role in the pancreatic It-cell proliferation and insulin secretion. In the present study. we have investigaled how sympathetic system mgulales the panrrealic n I rnerui nr ht an:ilyiing I'pinephi inn 1111 ), Norepinephrinc (NE) and /1-adrenergic receptor changes in the brain as (%eli is in the I swirls. Fill and NII showed a significant decrease in the brain regions, pancreas and plasma :rt 72Ius iller partial prurcrealectonty. We observed an increase in the circulating insulin levels at 72 hrs. Scatchard analysis using I CHI propranolol showed a significant increase in the number of loth the low affinity and high affinity t-adrenergic receplors in cerebral cortex and hypothalamus of partially pancreatectornised rats during peak DNA synthesis. The affinity of the receptors decrea,ed significantly in the low and high affinity receptors of cerebral cortex and the high affinity hypothalamic receptors. In file brain stein, low affinity receptors were increased significantly during regeneration whereas there was no change in the high affinity receptors. The pancreatic ff-adrenergic receptors were also up regulated at 72 firs after partial panerealectony. In vitro studies showed that /i-adrenergic receptors are positive regulators of islet cell proliferation and insulin secretion. Thus our results suggest that the t-adrenergic receptors are functionally enhanced during pancreatic regeneration, which in turn increases pancreatic ft-cell proliferation an(hilisulin secretion in wean hug rats.

Alterations in the muscarinic M I and M3 receptor gene expression in the brain stem during pancreatic regeneration and insulin secretion in weanling rats

Muscarinic M1 and M3 receptor changes in the brain stem during pancreatic regeneration were investigated. Brain stem acetylcholine esterase activity decreased at the time of regeneration . Sympathetic activity also decreased as indicated by the norepinephrine (NE) and epinephrine (EPI) content of adrenals and also in the plasma. Muscarinic Ml and M3 receptors showed reciprocal changes in the brain stem during regeneration. Muscairnic M1 receptor number decreased at time of regeneration without any change in the affinity. High affinity M3 receptors showed an increase in the number. The affinity did not show any change . The number of low affinity receptors decreased with decreased Kd at 72 hours after partial pancreatectomy. The Kd reversed to control value with a reversal of the number of receptors to near control value . Gene expression studies also showed a similar change in the mRNA level of Ml and M3 receptors . These alterations in the muscarinic receptors regulate sympathetic activity and maintain glucose level during pancreatic regeneration. Central muscarinic M1 and M3 receptor subtypes functional balance is suggested to regulate sympathetic and parasympathetic activity, which in turn control the islet cell proliferation and glucose homeostasis.

ALPHA2 Adrenergic and High Affinity Serotonergic Receptor Changes in the Brain Stem of Streptozotocin induced Diabetic Rats

The brain stems (13S) of streptozotocin (STZ)-diabetic rats were studied lo see the changes in neurotransmitter content and their receptor regulation. The norepinephrine (NE) content determined in the diabetic brain stems did ^ control. an E showed la while PI turnover hri content increased significantly compared N^r eNveFa o the recep significant increase. The alpha2 adrenergic receptor IneP utisoulinntreat d ratsetheNE contentt dec^ sled was significantly reduced during diabetes. in versedcto reanorm sed ulcrea e tK reatment the state. while EPI content remained increased as in die diabetic B,, for a]pha2 adrenergic receptors slw^nificantly while Unlabelled clonidine inhibited [31-I]NE binding in BS of control, diabetic and insulin treated ulations bindi diabetic rats showed that alpha2 adrenergicre^ punks cojnidiabetic animal the ligand bound sites with Hill slopes significantly away from unity. weaker to the low affinity site than in controls. Insulin treatment reversed[ this allumbmn to control levels. The displacement analysis using (-)-epinephrine age in control and diabetic animals revealed two populations of receptor affinidtyo=tat ss. In control animals, when GTP analogue added with epinephrine, the curve nagnlde caofnfitnroit yS model; but in the diabetic BS this effect `not aobserved. In bintact oth the diabetic data thus showlthat the effects of monovalent cations on affinity alphaz adrenergic receptors have a reduced affinity v due in stem ialtered Itscppeomson(5- regulation. The serotonin (5-HT) coat hydroxy) tryptophan (5-HTP) showed an increase and its breakdown metabolite (5-hydroxy) indoleacetic acid (5-I{IAA) showed a significant decrease. This showed that in serotonergic which l nerves there is a disturbance in both synthetic and breankduomwnbers pretma'med ana increased 5-HT. The high affinity serotonin receptor um ese serotonerg decrease in the receptor affinity. The insulin ^treatmentsturtiy showsha decreased serotonergic receptor kinetic parameters to control level. receptor function. These changes in adrenergic and serotonergic receptor function were suggested to be important in insulin function during STZ diabetes.

Brain 5HT2A Receptor Regulation by Tryptophan Supplementation in Streptozotocin Diabetic Rats

5-HT2A receptor binding parameters were studied in the cerebral cortex and brain stem of control, diabetic, insulin, insulin + tryptophan and tr3yptophan treated streptozotocin diabetic rats. Scatchard analysis using selective antagonist, [-H](±)2,3-dimethoxyphenyl-l-[2-(4-piperidine)- methanol] ([3H]MDL100907) in cerebral cortex of diabetic rats showed a significant decrease in dissociation constant (Kd) without any change in maximal binding (Bm). Competition binding studies in cerebral cortex using ketanserin against [3H]MDL100907 showed the appearance of an additional site in the low affinity region during diabetes. In the brain stem, Scatchard analysis showed a significant increase in Bmax and Kd. Displacement studies showed a shift in the receptor affinity towards a low affinity state. All these altered parameters in diabetes were reversed to control level by insulin, insulin + tryptophan and tryptophan treatments. Tryptophan treatment is suggested to reverse the altered 5-HT2Abinding and blood glucose level to control status by increasing the brain 5-HT content.

Enhancement of [m-methoxy 3H]MDL100907 binding to 5HT 2A receptors in cerebral cortex and brain stem of streptozotocin induced diabetic rats

5-Hydroxytryptamine2A (5-HT2A) receptor kinetics was studied in cerebral cortex and brain stem of streptozotocin (STZ) induced diabetic rats. Scatchard analysis with [3H] (±) 2,3dimethoxyphenyl-l-[2-(4-piperidine)-methanol] ([3H]MDL100907) in cerebral cortex showed no significant change in maximal binding (Bmax) in diabetic rats compared to controls. Dissociation constant (K) of diabetic rats showed a significant decrease (p < 0.05) in cerebral cortex, which was reversed to normal by insulin treatment. Competition studies of [3H]MDL100907 binding in cerebral cortex with ketanserin showed the appearance of an additional low affinity site for 5-HT2A receptors in diabetic state, which was reversed to control pattern by insulin treatment. In brain stem, scatchard analysis showed a significant increase (p < 0.05) in Bmax accompanied by a significant increase (p < 0.05) in Kd. Competition analysis in brain stem also showed a shift in affinity towards a low affinity State for 5-HT2A receptors. All these parameters were reversed to control level by insulin treatment. These results show that in cerebral cortex there is an increase in affinity of 5-HT2A receptors without any change in its number and in the case of brain stem there is an increase in number of 5HT2A receptors accompanied by a decrease in its affinity during diabetes. Thus, from the results we suggest that the increase in affinity of 5-HT2A receptors in cerebral cortex and upregulation of 5-HT2A receptors in brain stem may lead to altered neuronal function in diabetes.

PEER TO PEER CACHE RESOLUTION MECHANISM FOR MOBILE AD HOC NETWORKS

In this paper we investigate the problem of cache resolution in a mobile peer to peer ad hoc network. In our vision cache resolution should satisfy the following requirements: (i) it should result in low message overhead and (ii) the information should be retrieved with minimum delay. In this paper, we show that these goals can be achieved by splitting the one hop neighbours in to two sets based on the transmission range. The proposed approach reduces the number of messages flooded in to the network to find the requested data. This scheme is fully distributed and comes at very low cost in terms of cache overhead. The experimental results gives a promising result based on the metrics of studies.

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.