Extraction of Gliomas from 3D MRI Images using Convolution Kernel Processing and Adaptive Thresholding

In this work, the evaluation of segmentation techniques towards the detection tumours or gliomas on BraTs 2018 data sets on T2 weighted 3D MRI images is carried out. Techniques experimented include pixel wise Linear Binary Pattern (LBP), region growing and Otsu thresholding to compare the efficiency of the same with proposed approach. In this work, 3D MRI images are initially subject to slicing from which about 10% from the top and bottom are normalized as those slices does not possess any significant details (empty slices). Further, the enhancement of tumour/gliomas regions is performed on normalized slices using high boost ‘convolution kernel processing in various trials with varying kernel sizes of high boost convolution kernel. The glioma regions enhanced using kernel are subject to local adaptive thresholding technique for extraction of gliomas from normalized slices. The detected gliomas are compared with the results of ground truth segmentation to determine the effectiveness of the proposed technique. It is observed in 80% of slices are successful in detection of gliomas both in case of Low Grade Glioma (LGG) and High Grade Glioma (HGG). By analyzing the results, it is observed that proposed method has outperformed with average Jaccard and Dice similarity recorded as 87.20% and 89.18% for HGG and where as in case of LGG it is found to be 83.77% and 87.74%.