The aim of the work is to develop an algorithm for extracting local extrema of images with low computational complexity and high accuracy. The known algorithms for block search for local extrema have low computational complexity, but only strict maxima and minima are distinguished without errors. The morphological search gives accurate results, in which the extreme areas are formed by non-strict extrema, however, it has high computational complexity. This paper proposes a block-segment search algorithm for local extrema of images based on space-oriented masks. The essence of the algorithm is to search for single-pixel local extrema and regions of uniform brightness, comparing the values of their boundary pixels with the values of the corresponding pixels of adjacent regions: the region is a local maximum (minimum) if the values of all its boundary pixels are larger (smaller) or equal to the values of all adjacent pixels. The developed algorithm, as well as the morphological search algorithm, allow to detect all single-pixel local extrema, as well as extreme areas, which exceeds the block search algorithms. At the same time, the developed algorithm in comparison with the morphological search algorithm requires much less time and RAM. [ABSTRACT FROM AUTHOR]