Abstract:
Texture analysis studies have been increasingly explored in
neuroradiology in the recent yeares, as it enabling diseases
characterization and quantification of disease distribution. The aim of this
study was to characterize white matter lesions on the brain in MR images
using texture analysis. This analytical study had been conducted at
Antalya medical center in the period from October 2018 to March 2021
by using study of 1646 brain MR images selected conveniently for
patients having Glioma, Multiple Sclerosis (MS) and Small Vessels
Diseases (SVD) , then these imeges entered to interactive data language
program to extract the textural features from the normal tissues and the
white matter lesions, then these extracted features entered to statistical
package social science for analysis. And the results reveal that; the best
textural features for discrimination of the Glioma from the normal tissues
in all MR imaging sequences were the mean, entropy, Gray-Level
Nonuniformity and Run-Length Nonuniformity. While for the MS
plaques the features were the mean, variance, entropy and Run
Percentage textural features. Also the best features for differentiation the
SVD from normal tissues in all MRI sequences were the mean, variance,
energy, entropy, Gray-Level Nonuniformity and Run Percentage. In
conclusion texture analysis statistics successfully discriminate the white
matter lesions from normal brain tissues in all MRI sequences, firstly
when using the first order statistics on the Glioma, MS and SVD the
imaging sequence that shows the highest sensitivity in discrimination the
lesion from normal tissues were T2=98.2%, T1+C=99.3% and
T1+C=97.5% respectively; and when using the higher order statistics the
highest sensitivity were in T1+C=93.6%, T1+C=99.3% and T1=97.5%
correspondingly.
