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.

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

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