A problem of computer vision applications is to detect regions of interest under different imaging conditions. The state-of-the-art maximally stable extremal regions (MSERs) detects affine covariant regions by applying all possible thresholds on the input image, and through three main steps including: (1) making a component tree of extremal regions' evolution; (2) obtaining region stability criterion; and (3) cleaning up. The MSER performs very well, but, it does not consider any information about the boundaries of the regions, which are important for detecting repeatable extremal regions. We have shown in this paper that employing prior information about boundaries of regions results in a novel region detector algorithm that not only outperforms MSER, but avoids the MSER's rather complicated steps of enumeration and the cleaning up. To employ the information about the region boundaries, we introduce maxima of gradient magnitudes (MGMs) which are shown to be points that are mostly around the boundaries of the regions. Having found the MGMs, the method obtains a global criterion for each level of the input image which is used to find extremum levels (ELs). The found ELs are then used to detect extremal regions. The proposed algorithm which is called extremal regions of extremum levels (EREL) has been tested on the public benchmark data set of Mikolajczyk. The obtained experimental results show that the inclusion of region boundaries through MGMs, results in a detector that detects regions with high repeatability scores and is more robust against noise compared with MSER.