Abstract:
This study aimed to characterize brain glioma in magnetic resonance images using image texture
analysis techniques in order to recognize the tumor and surrounding tissues by means of textural
features. This an analytical case control study was conducted in radiation oncology department at
radiation and isotopes center of Khartoum (RICK), which included 100 patients underwent MRI for
brain (50 with brain glioma and the rest with normal MRI (case control) scan), FLAIR, T2, T1, and
T1 with contrast sequence was performed then the image extracted as DICOM images and then
converted to TIFF format which used as input data for an algorithm generated using IDL
(interactive data language) for textural features extraction. Three basic textural features types was
used to classify the brain images using five different window sizes (3x3, 5x5, 10x10, 15x15, and
20x20 pixels) which are first order statistics (FOS), second order statistics (SGLD), and diagonal
features (dSGLD), to recognizes 4 different classes (brain gray and white matter, tumor,
background and CSF); further analysis and image segmentations was performed to remove
background from the images for purpose of image enhancement. The extracted feature classified
using linear discriminant analysis. The result showed that the classification accuracy, sensitivity and
specificity according to window sizes was (99.5%, 98.4% and 100%), (98.5%, 95.7% and 100%),
(99.1%, 98.8% and 99.3%), (98.1%, 94.3% and 100%), and (96.1%, 90.0% and 98.8%) respectively
for brain glioma. This study implies that 3x3 window gives a higher classification accuracy while
the most significant features for classification includes; difference average of SGLD, mean and
entropy of FOS.
