Pre-processing is a critical step in any data-driven study, particularly in the field of medical imaging, where it significantly enhances the reliability of disease and disorder diagnosis. In this context, medical image segmentation allows for more precise data analysis by isolating the regions of interest. Accurate segmentation of these regions can reveal influential variabilities in analysis, potentially leading to unique scientific findings. This article presents a novel under-segmentation error correction technique specifically designed for whole-brain segmentation. Additionally, it performs a set of pre-processing steps for the structural magnetic resonance imaging (sMRI) images, which are necessary to maintain the structural integrity and uniformity of MRI scans across different subjects. The proposed algorithm effectively eliminates under-segmentation errors, thereby improving the accuracy of whole-brain segmentation, particularly for structurally intact brain images.