AFM Image Segmentation Based on Wave Growth of Local Maximum Regions with their Selection in Order of Decreasing Values

Abstract

The problem of determining the number of objects in atomic force microscopy (AFM) images is considered. For the automatic (without operator participation) solution of this problem, segmentation is used, dividing images into areas containing objects of interest. Known segmentation algorithms based on the morphological watershed, defining the boundaries of areas by local minima of pixel brightness, having significant segmentation errors of AFM images and high computational complexity. Less computationally complex segmentation algorithms based on wave growth of regions require preliminary determination of the starting points of growth on AFM images under the control of an operator. Algorithms for growing regions without preliminary selection of the starting points of growth have the least computational complexity, but they segment the AFM image with a large error. To improve the accuracy of automatic determination of the number of objects in AFM images, a model and an algorithm for the wave growth of the regions of local maxima with their selection in decreasing order of values are proposed, which differ in the use of a brightness threshold varying from maximum to minimum to select growth pixels of regions or pixels attached to pixels of adjacent existing areas. The model provides parallel expansion of the boundaries of areas and automatic determination of the initial growth pixels during the segmentation process. The proposed model and algorithm make it possible to eliminate segmentation error characteristic of the marker watershed, growing areas and the Vincent – Sulli watershed, and thereby increase the accuracy of determining the number of objects in atomic force microscopy images.