Estimating the salient regions of an image plays a key role in scene analysis and image understanding. We can also apply saliency-based image processing techniques to image compression, evaluation, and effective searching methods. One of the most difficult problems is estimating the regions before recognizing what is in the image -- a problem that can be solved by accessing information of the low-level structures of objects in the image. This paper describes a method for estimating salient regions in images based on the distribution stability of local extrema of luminance during image blurring. Under blurring conditions, if an object's region has a more stable structure compared to another area, it must be more salient, so the saliency of these regions must be defined based on their stability for blurring. In the developed method, the local extrema of images are used to describe the complexity of the image's objects and background. Salient regions are estimated based on the stability of the local extrema for the blurring parameter. Experiments were conducted to compare the estimated result of salient regions and the psychophysical result obtained from the analysis of eye movement recordings. Results show that our method successfully extracts salient regions of natural images.