Low Level Jet stream of Asian Summer Monsoon and its Variability

The main objective of the of present study are to study the intraseasonal variability of LLJ and its relation with convective heating of the atmosphere, to establish whether LLJ splits into two branches over the Arabian sea as widely believed, the role of horizonatal wind shear of LLJ in the episodes of intense rainfall events observed over the west coast of India, to perform atmospheric modeling work to test whether small (meso) scale vortices form during intense rainfall events along the west coast; and to study the relation between LLJ and monsoon depression genesis. The results of a study on the evolution of Low Level Jetstream (LLJ) prior to the formation of monsoon depressions are presented. A synoptic model of the temporal evolution of monsoon depression has been produced. There is a systematic temporal evolution of the field of deep convection strength and position of the LLJ axis leading to the genesis of monsoon depression. One of the significant outcomes of the present thesis is that the LLJ plays an important role in the intraseasonal and the interannual variability of Indian monsoon activity. Convection and rainfall are dependent mainly on the cyclonic vorticity in the boundary layer associated with LLJ. Monsoon depression genesis and the episodes of very heavy rainfall along the west coast of India are closely related to the cyclonic shear of the LLJ in the boundary layer and the associated deep convection. Case studies by a mesoscale numerical model (MM5) have shown that the heavy rainfall episodes along the west coast of India are associated with generation of mesoscale cyclonic vortices in the boundary layer.

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.