a problem of computer vision applications is to detect regions of interest under dif- ferent imaging conditions. the state-of-the-art maximally stable extremal regions (mser) 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 (enumeration), 2) obtaining region stability criterion, and 3) cleaning up. 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 step 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 (gc) 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 lev- els (erel) has been tested on the public benchmark dataset of mikolajczyk [111]. the obtained experimental results show that the proposed erel method is robust against gaussian noise and outperforms the state-of-the-art methods in terms of the accuracy of the repeatable detected regions.