This paper presents a novel algorithm for extracting regions of interest (ROIs) from images in an unsupervised way. It relies on the information provided by two computational models of bottom-up visual attention, encoded in the form of the image's salient points-of-attention (POAs) and areas-of-attention (AOAs). The proposed method combines these POAs and AOAs to generate binary masks that correspond to the ROIs within the image. First, each AOA is binarized through an adapted relaxation algorithm where the histogram entropy of the AOA measurement is the stop criterion of the iterative process. The AOAs are also smoothed with a Gaussian pyramid followed by interpolation. Next, the binary representation of the AOAs, the smoothed version of the AOAs, and the POAs are converted in a mask that covers the salient ROIs of the image. The proposed ROI extraction algorithm does not impose any constraints on the number or distribution of salient regions in the input image. Qualitative and quantitative results show that the proposed method performs very well in a wide range of images, whether natural or man-made, from simple images of objects against a homogeneous background to complex cluttered scenes.