Background and Purpose To apply a texture analysis of apparent diffusion coefficient (ADC)
maps to evaluate glioma heterogeneity, which was correlated with tumor grade. Materials …

Purpose An accurate prediction of tumour response to therapy is fundamental in oncology,
so as to prompt personalised treatment options if needed. The aim of this study was to …

Objective: The main objective of this retrospective work was the study of three-dimensional
(3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T 1 …

Brain tumors vary in their position, mass, nature, and consistency of these lesions. Due to
the similarities found between brain lesions and normal tissues, many challenges are faced …

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an
indispensable imaging technique in clinical neuroimaging that quantitatively assesses the …

Deep learning has enabled the creation of several approaches for segmenting brain tumors
using convolutional neural networks. These methods have come about as a direct result of …

This work aims to identify non-invasive quantitative parameters from three-dimensional brain
magnetic resonance images in order:(1) to classify brain tumor (glioma) as low grade (LG) or …

This study presents a novel non-invasive quantitative feature set using magnetic resonance
imaging (MRI) for diagnosis of brain tumor and their grade classification. Texture features …

In this research, Plasma physics and digital image processing technique are utilized.
Dielectric Barrier Discharges (DBD) plasma at atmospheric pressure is used for the purpose …

detection of the tumour in a human brain is a challenging problem, due to the arrangement
of the tumour cells in the brain. This paper presents an analytical method that improves the …