Automatic Brain Tumor Detection and Segmentation from MRI Images

Abstract

A brain tumor is an abnormal growth of tissue in the central spine or brain that can cause the brain to become more pressured and interfere with normal brain function. Early detection of a brain tumor is extremely challenging for medical professionals. MRI images can be greatly impacted by noise and other environmental disruptions. As a result, diagnosing the tumor and its causes becomes challenging for medical professionals. Therefore, a methodology is presented to detect brain tumors and segment them from medical image. According to the studies, the region growing algorithm initializes the seed point either manually or semi-manually, which has an impact on the segmentation outcome. Therefore, an improved region growing algorithm is suggested for automatic seed point initialization. This paper presents an automated and integrated framework for the highly accurate detection and segmentation of brain tumors. The suggested methodology starts with image pre-processing to enhance image quality and reduce noise. The next step involves using the skull stripping developed algorithm, which is specifically made for precise brain mask extraction. The generated mask is crucial in automatically determining the tumor region's seed point, which removes the need for human intervention and reduces the variability of the results. Lastly, the entire tumor region is segmented using a region-growing algorithm that starts with the identified seed. Experimental results demonstrate that our proposed technique achieves robust performance and accurate results in detecting and segmenting brain tumor boundaries in MRI images, making it a promising tool for supporting clinical decision-making.