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Caenorhabditis elegans identification method and system for easy image segmentation

An image segmentation and recognition method technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of uneven brightness, identification of nematodes, etc., and achieve the effect of good promotion and use value, convenient calculation and filter recognition

Active Publication Date: 2018-03-16
QILU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical task of the present invention is to address the above deficiencies, provide a nematode identification method and system that is easy to image segmentation, and solve the problem that nematodes cannot be easily and quickly identified from nematode experimental images with uneven background brightness and black borders in the prior art question

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  • Caenorhabditis elegans identification method and system for easy image segmentation

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Embodiment 1

[0048] The nematode identification method that is easy to image segmentation of the present invention utilizes computer to carry out image acquisition and image processing and identify nematode; comprises the following steps:

[0049] (1), read in the experimental image of the nematode on the microwell plate;

[0050] (2) Calculating the gray-level co-occurrence matrix and the contrast feature value in the nematode experimental image by means of a sliding window, and obtaining the contrast feature value centered on each pixel, and converting to generate a contrast feature image;

[0051] (3) Preliminarily segment the contrast characteristic image, and identify the foreground object according to the contrast difference;

[0052] (4) Filtering the above-mentioned segmented foreground objects to identify nematodes.

Embodiment 2

[0054] The nematode identification method that is easy to image segmentation of the present invention utilizes computer to carry out image acquisition and image processing and identify nematode; comprises the following steps:

[0055] (1), read in the experimental image of the nematode on the microwell plate;

[0056](2) Calculate the gray level co-occurrence matrix and contrast eigenvalues ​​by means of a sliding window in the nematode experimental image, and obtain the contrast eigenvalues ​​centered on each pixel, and convert to generate contrast eigenimages;

[0057] (3) Preliminarily segment the contrast characteristic image, and identify the foreground object according to the contrast difference;

[0058] (4) Filtering the above-mentioned segmented foreground objects to identify nematodes.

[0059] In step (2), each window slides over the covered sub-image, calculates the gray-scale co-occurrence matrix and contrast eigenvalue in the sub-image area, and assigns the cont...

Embodiment 3

[0061] The nematode identification method that is easy to image segmentation of the present invention utilizes computer to carry out image acquisition and image processing and identify nematode; comprises the following steps:

[0062] (1), read in the experimental image of the nematode on the microwell plate;

[0063] (2) Calculate the gray level co-occurrence matrix and contrast eigenvalues ​​by means of a sliding window in the nematode experimental image, and obtain the contrast eigenvalues ​​centered on each pixel, and convert to generate contrast eigenimages;

[0064] (3) Preliminarily segment the contrast characteristic image, and identify the foreground object according to the contrast difference;

[0065] (4) Filtering the above-mentioned segmented foreground objects to identify nematodes.

[0066] In the step (2), each window slides over the sub-image formed by covering, calculates the gray-level co-occurrence matrix and the contrast feature value in the sub-image are...

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Abstract

The present invention discloses a Caenorhabditis elegans identification method and system for easy image segmentation, relates to the technical field of biological image processing, and solves the problem that in the prior art, the Caenorhabditis elegans cannot be simply and quickly identified from a Caenorhabditis elegans experimental image with uneven background brightness and a black border. The Caenorhabditis elegans identification method comprises: reading an experimental image of a Caenorhabditis elegans on a microwell plate; calculating a gray co-occurrence matrix and a contrast featurevalue in the Caenorhabditis elegans experimental image in a sliding window manner, obtaining a contrast feature value by taking each pixel as a center, and converting the contrast feature value to generate a contrast feature image; initially segmenting the contrast feature image, and identifying foreground objects according to the contrast difference; and filtering the segmented foreground objects to identify the Caenorhabditis elegans. According to the method and the system disclosed by the present invention, a microwell plate is placed on the rack, and the microwell plate is placed under adigital microscope; the digital microscope is in communication connection with a computer processing control terminal; and the method and the system have the advantages that the Caenorhabditis elegansidentification is simple and the resources occupied are less.

Description

technical field [0001] The invention relates to the technical field of biological image processing, in particular to a nematode identification method and system which are easy to image segmentation. Background technique [0002] In the field of biological image processing technology, Caenorhabditis elegans (Caenorhabditis elegans) is a model animal with many applications. It is small in size, about 1 mm in length, and easy to cultivate. Common Caenorhabditis elegans has an average lifespan of about two to three weeks and a developmental time of about three days in a culture environment at 20°C in the laboratory. In 1974 Brenner chose to use C. elegans as a model organism to study developmental and neuroscience issues. Image acquisition and image processing are used in many high-throughput screening efforts using C. elegans. The image data produced in high-throughput screening experiments far exceeds the ability of manual inspection and analysis, making researchers resort t...

Claims

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Application Information

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IPC IPC(8): G06T7/73G06T7/194
CPCG06T7/194G06T7/73
Inventor 陈维洋李伟伟
Owner QILU UNIV OF TECH
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