Image segmentation method based on watershed algorithm and morphological marker

Abstract

The invention provides an image segmentation method based on a watershed algorithm and a morphological marker. The method comprises the steps that median filtering is carried out on a gray level image to obtain a filtered image; an OTSU method is carried out on the filtered image to obtain a binary image; the binary image is processed through a morphological algorithm based on reconstruction to obtain a characteristic marked image; the characteristic marked image is transformed through the watershed algorithm to obtain a segmented image. According to the image segmentation method, the OTSU method and median filtering are utilized for filtering out impurities and noisy points in the image, the image is adopted as the primary mark source of the watershed algorithm, and the interference of noise is effectively eliminated; a morphological operation method is adopted, the information of an effective area cannot be lost, meanwhile, certain fuzzy areas or connected areas can be separated, and the integrity and consistency of image segmentation are guaranteed. Connected domain calculation is combined, the invalid target and information of non-noisy points can be removed, the marker of the watershed algorithm is precisely located, and the over-segmentation phenomenon is eliminated.

Description

technical field [0001] The invention relates to an image segmentation processing method, in particular to an image segmentation processing method based on a watershed algorithm and morphological markers, which can be used for various image segmentation including micro-nano particle distribution, cell or defect detection. Background technique [0002] Image segmentation is a very important step in image processing and detection analysis. It can divide the target image into several regions with certain similarity and consistency characteristics, accurately locate these regions and further analyze some of their characteristics. and processing. [0003] Image segmentation is usually used for further image analysis, detection, evaluation, etc. The accuracy of segmentation and the consistency of regions will directly affect the value of subsequent work. The mainstream image segmentation methods are generally divided into two categories, one is threshold-based segmentation, and th...

AI Technical Summary

Problems solved by technology

[0008] The image segmentation method provided by the invention can not only ensure the accuracy of marking, eliminate invalid information such as impurities and noise, but also effectively solve the problem of over-segmentation when the watershed algorithm is used to segment images

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