Automatic Skull Stripping of MRI Head Images Based on Adaptive Gamma Transform.

kull stripping is regarded as an important pre-processing step by many neuroimaging processing applications. An appropriate skull stripping is crucial because of the complex anatomical makeup of the brain and variations in brain MRI intensity. The removal of the skull region for clinical analysis in brain segmentation tasks is essentially the process of "skull stripping," and its accuracy and effectiveness are very important for diagnostic purposes. It is thought to be a difficult task because it calls for more precise and thorough methods for separating the different regions of the brain and the skull. Consequently, a technique is suggested for skull stripping by improving the contrast of the brain image using Adaptive gamma correction (AGC), which sets its settings dynamically based on the properties of the input image. In addition, the largest connected components, morphological image processing technique, and image multiplications are used in the proposed skull stripping method. The Br35H::Brain Tumor Detection 2020 dataset and Brain MRI Images for Brain Tumor Detection dataset have been used for the experimentation. The results of the experiments show that the proposed image enhancement and skull removal techniques work effectively with an accuracy rate of 96%. [ABSTRACT FROM AUTHOR]