Distribution uniformity assessment method based on watershed algorithm and minimum spanning tree

Abstract

The invention provides a distribution uniformity assessment method based on a watershed algorithm and a minimum spanning tree. The method comprises the steps that an image is subjected to gray processing and median filtering; binarization processing is conducted through an OTSU method; morphological operation is conducted to obtain a feature tag image; segmentation is conducted through the watershed algorithm; an adjoining matrix composed of centroids of segmented regions in the segmented image and distances between the centroids is calculated; the minimum spanning tree is calculated through a Prim algorithm; the distribution uniformity of particles or spots in the image is analyzed based on the minimum spanning tree. According to the method, interference, noise and other ineffective information of the image are filtered out, a tag source is provided for the watershed algorithm, and mistaken segmentation caused by inconsistency of sizes of the particles or the spots and over segmentation caused by the noise are avoided; segmentation based on the watershed algorithm better represents the relation among the particles or the spots and the relation between the particles or the spots and overall distribution; since the Prim algorithm is used for obtaining the minimum spanning tree, the time complexity is low, the efficiency is high, and the assessment of distribution uniformity is more accurate.

Description

technical field [0001] The present invention relates to a distribution image uniformity evaluation method, in particular to a distribution uniformity evaluation algorithm based on watershed algorithm and minimum spanning tree, which can be used for micro-nano particle distribution, metallographic analysis, defect detection distribution and cell biological morphology Uniformity assessment analysis of the distribution. Background technique [0002] Uniformity analysis is an important part of distribution detection methods such as image analysis and metallographic detection. It is an index to study the uniformity of the distribution of target objects in the overall area in the image. By transforming or defining the target objects in these distribution images as binarized connected regions, or centroid coordinate information, the uniformity and uniformity of the distribution can be accurately analyzed. [0003] Image uniformity evaluation usually exists as a means of evaluating...

AI Technical Summary

Problems solved by technology

[0007] These evaluation and analysis methods do not consider the size of the actual target object, so when simplifying the processing, the segmentation is easy to cut to the actual target object, resulting in invalid area division, and because most of the methods only consider the target in the local area The relationship between the number and the uniformity does not deeply study the importance of the core index of the distance between the targets for the uniformity analysis, so the accuracy of these methods is not high

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