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.

Infrared and polarized Raman spectra of Cu(HSeO3)2 • H20 single crystal

Infrared and polarized Raman spectra of Cu(HSeO3) 2 - H20 single crystal have been recorded and analysed. The appearance of non-degenerate Se-OH stretching vibrations in the ~x: and ~y: polarizations of Raman spectra indicate distortion of the HSeO~- ion in the Cu(HSeO3)2 - H20 crystal. The low wavenumber values obtained for the symmetric and asymmetric stretching vibrations of the HSeO 3 ion are consistent with the strong hydrogen bonding and the influence of Jahn-Teller distortion as predicted in X-ray diffraction data. The shifting of the stretching and bending vibrations of the hydroxyl groups and water molecules from the free state values also confirms the strong hydrogen bonding in this crystal. Broad bands observed for both stretching and bending regions become sharp in the Raman spectrum recorded at 77 K. A doublet appears for the Se-OH stretching mode at this temperature indicating the settling of protons in an ordered position and the absence of intrabond proton tunnelling

Temperature dependent polarized Raman spectra of nonaaqualanthanoid (Pr) single crystal

Polarized Raman spectral changes with respect to temperature were investigated for Pr(BrO3)3·9H2O single crystals. FTIR spectra of hydrated and deuterated analogues were also recorded and analysed. Temperature dependent Raman spectral variation have been explained with the help of the thermograms recorded for the crystal. Factor group analysis could propose the appearance ofBrO3 ions at sites corresponding to C3v (4) and D3h (2). Analysis of the vibrational bands at room temperature confirms a distorted C3v symmetry for the BrO3 ion in the crystal. From the vibrations of water molecules, hydrogen bonds of varying strengths have also been identified in the crystal. The appearance υ1 mode of BrO3− anion at lower wavenumber region is attributed to the attachment of hydrogen atoms to the BrO3− anion. At high temperatures, structural rearrangement is taking place for bothH2Omolecule and BrO3 ions leading to the loss ofwater molecules and structural reorientation of bromate ions causing phase transition of the crystal at the temperature of 447 K.

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.