Parasite egg identification method based on multi-feature fusion

A multi-feature fusion and recognition method technology, which is applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of overlapping feature value ranges, image features that cannot accurately reflect image characteristics, etc.

Inactive Publication Date: 2015-03-04
STATION OF VIRUS PREVENTION & CONTROL CHINA DISEASES PREVENTION & CONTROL CENT
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  • Claims
  • Application Information

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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

Method used

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  • Parasite egg identification method based on multi-feature fusion
  • Parasite egg identification method based on multi-feature fusion
  • Parasite egg identification method based on multi-feature fusion

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Embodiment 1

[0097] A fully automatic identification method for parasite eggs based on multi-feature fusion, including the following process (see flow chart figure 1 ):

[0098] a) A step of image preprocessing, which is to normalize the brightness of the image information obtained by the photomicrograph equipment, and to grayscale the normalized image to generate a normalized grayscale image, and then to normalize the whole A picture is sharpened based on Gaussian filtering to obtain an image with a sharpened edge of the egg;

[0099] b) A step of performing mean shift on the image with sharpened egg edges to find the eggs, using the mean shift algorithm to segment the target image, obtaining the color feature vector of the image, planning and finding the best color vector based on the color vector The target area is the area judged to be an egg;

[0100] c) A step of performing target acquisition on the egg image based on the region identified as the egg. Based on the above shape segm...

Embodiment 2

[0135] A method for artificially assisted identification of parasite worm eggs based on multi-feature fusion is characterized in that it includes the following process (flow chart is shown in Figure 6 ):

[0136] a) A step of image preprocessing. Normalize the brightness of the image information acquired by the micrographic equipment, grayscale the normalized image, generate a normalized grayscale image, and then perform sharpening processing on the entire image based on Gaussian filtering , to obtain an image with sharpened edges of eggs;

[0137] b) A step of using the enhanced Grab Cut method to artificially assist the identification of the edge-sharpened image of the eggs to find the eggs. In the step of target segmentation, the enhanced Grab Cut method is adopted, that is, the user provides a limited box for manual support, more accurately divides the foreground and background, obtains the color feature vector of the image, plans and finds the best target area based on...

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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 ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V10/752G06V10/757
Inventor 沈海默陈韶红陈家旭
Owner STATION OF VIRUS PREVENTION & CONTROL CHINA DISEASES PREVENTION & CONTROL CENT
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