Brain Tumor Extraction using Adaptive Threshold Selection Network

Abstract

Brain tumor extraction has increased its potential in brain image processing. Extraction of tumor region from brain MRI image is still a trivial task. However, most of the contemporary proposals has devised sparse representation and convolutional neural networks to address this issue. These methods suffer from high computational cost and additional memory requirements. Threshold-based techniques are efficient for fast brain tumor extraction. In this paper, we have proposed an adaptive threshold selection approach to address it. The proposed method has implemented Adaptive Threshold Selection Network (ATSN) with two phases: training and testing with a common pre-processing step. In training phase, pre-processed train images and their ground truth images are used to achieve an adaptive threshold. Testing phase extracts tumor segment from the pre-processed test image using thresholding. We considered brain tumor dataset of 2295 images with three types of brain diseases: meningioma, pituitary and glioma tumor. Performance of proposed method has been evaluated using five essential measures: dice similarity, jaccard coefficient, accuracy, sensitivity, and specificity. Proposed method achieved superior results in terms of specified measures except accuracy and sensitivity while comparing with its competing methods.