A visual attention-based algorithm for brain tumor detection using an on-center saliency map and a superpixel-based framework

Abstract

Brain tumors are life-threatening and are typically identified by experts using imaging modalities like Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET). However, any error due to human intervention in brain anomaly detection can have devastating consequences. This study proposes a tumor detection algorithm for brain MRI images. Previous research into tumor detection has drawbacks, paving the way for further investigations. A visual attention-based technique for tumor detection is proposed to overcome these drawbacks. Brain tumors have a wide range of intensity, varying from inner matter-alike intensity to skull-alike intensity, making them difficult to threshold. Thus, a unique approach to threshold using entropy has been utilized. An on-center saliency map accurately captures the biological visual attention-focused tumorous region from the original image. Later, a superpixel-based framework has been proposed and used to capture the true structure of the tumor. Finally, it was experimentally shown that the proposed algorithm outperforms the existing algorithms for brain tumor detection.