Parasite egg identification method based on multi-feature fusion

Abstract

Provided is a parasite egg identification method based on multi-feature fusion. The method comprises the steps that one step of image preprocessing, in which brightness normalization processing and sharpening processing based on Gaussian filtering are performed on image information acquired by micro-photographic equipment, is performed so that an egg edge sharpening image is obtained; segmentation processing is performed on a target picture by using a mean shift algorithm so that areas judged to be eggs are acquired; binary processing is performed on each candidate edge area according to the established information of the parasite egg shape edge areas to be identified, and target acquisition is performed by adopting a boundary tracking algorithm according to the boundary of the egg areas so that the segmented egg images are obtained; the specified feature values of the egg images are intercepted to be stored in a preset feature database; and the acquired feature values are substituted into a general database by adopting a KNN (k=3) algorithm based on relative distance, and the category of the eggs is judged based on the KNN algorithm. Egg identification accuracy exceeds 90% so that a relatively ideal result is achieved.

Description

Technical field: [0001] The invention belongs to the technical field of image recognition, and in particular relates to a method for identifying eggs, in particular to a method for identifying parasite eggs based on multi-feature fusion. Background technique: [0002] Parasitic diseases are still one of the global public health problems. Microscopic examination of worm eggs is one of the key control techniques, and it is also a basic link in the analysis of parasite morphological characteristics and subsequent biological research. The identification of parasite eggs cannot be carried out with automated instruments like blood cell analysis. For a long time, it has only relied on human eyes to observe and distinguish under a microscope. However, identifying different eggs among numerous parasite samples is a cumbersome task and requires specialized training for technicians. At present, the method of manually smearing specimens and visually identifying them under a microscope ...

AI Technical Summary

Problems solved by technology

[0012] c) The extracted image features cannot accurately reflect the image characteristics, so that the feature value ranges of various recognition objects overlap more, and a complex classification algorithm has to be used to improve the recognition rate, which is different from the five-class blood cell analysis currently used in hospitals. Compared with the high-efficiency operation of the instrument, the fastest recognition processing time of 15s in foreign systems still needs to be improved.

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