Background and Objective: Image-guided neurosurgery uses information from a wide spectrum of methods to inform the neurosurgeon's judgement about which tissue to resect and which to spare. Imaging data are registered to the patient's skull so that they correspond to the intraoperative macro- and microscopic view. The correspondence between imaging and optical systems breaks down during surgery,...

Magnetic resonance imaging (MRI) provides detailed anatomic information of any part of the body. In this method a hybrid approach for classification of brain tissue in MRI based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) wavelet based texture feature are extracted from normal and tumor region by using HAAR wavelet. These features are given as input to the SVM classifi...

Small-animal MRI with high field strength allows imaging of the living animal. However, spatial resolution in in vivo brain imaging is limited by the scanning time. Measurements of fixated mouse brains allow longer measurement time, but fixation procedures are time consuming, since the process of fixation may take several weeks. We here present a quick and simple post-mortem approach without fi...

Early adolescence is a time of rapid change in neuroanatomy and sexual development. Precision in tracking changes in brain morphology with structural MRI requires image segmentation with minimal error. Here, we compared two approaches to achieve segmentation by image registration with an atlas to quantify regional brain structural development over a 7-month interval in normal, early adolescent ...

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field...

BACKGROUND Brain tissue segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) are important in neuroradiological applications. Quantitative Mri (qMRI) allows segmentation based on physical tissue properties, and the dependencies on MR scanner settings are removed. Brain tissue groups into clusters in the three dimensional space formed by the qMRI parameters R1, R2 a...

Our objective was to investigate diverse clinical antecedents of total and regional brain volume abnormalities and white matter hyperintensity volume on term MRI in extremely low birth weight (birth weight ≤1000 g) survivors. A consecutive cohort of extremely low birth weight infants who survived to 38 weeks postmenstrual age (n = 122) and a control group of 16 healthy term newborns underwent b...

Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed literature segmentation, this paper proposes a lightweight implementation U-Net. Apart from providing real-time scans, architecture does not need large...

The recent driven equilibrium single-pulse observation of T1 (DESPOT1) approach permits real-time clinical acquisition of large-volume and high-isotropic-resolution T1 mapping of MR tissue parameters with improved uniformity. It is assumed that the quantitative nature of maps will facilitate clinical applications such as disease diagnosis and comparison across subjects. However, there is not ye...

AbstractMRI is conventionally employed in neonatal brain diagnosis and research studies. However, the traditional segmentation protocols omit differentiation between heterogeneous white matter (WM) tissue zones that rapidly evolve change during early development. There a reported correlations of characteristics transient WM compartments (including periventricular regions, subplate, etc.) with m...