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Visual saliency and SIFT characteristic based echinococcosis protoscolex survival rate detection method

A technology of echinococcus and detection method, applied in the field of detection, can solve problems such as low efficiency, heavy workload, error, etc., achieve the effect of solving calculation too complicated, SIFT feature stability, and improving calculation efficiency

Active Publication Date: 2018-12-07
THE FIRST TEACHING HOSPITAL OF XINJIANG MEDICAL UNIVERCITY
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  • Claims
  • Application Information

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Problems solved by technology

[0004] The present invention provides a method for detecting the survival rate of Echinococcus protoscoleum with visual salience and SIFT features, which overcomes the above-mentioned deficiencies in the prior art, and can effectively solve the problem that human intervention is required in the identification process of the existing method Too many, the proposed image features cannot reflect the image characteristics, so that the feature value ranges of various recognition objects overlap more, etc., resulting in low efficiency, heavy workload and human error problems

Method used

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  • Visual saliency and SIFT characteristic based echinococcosis protoscolex survival rate detection method
  • Visual saliency and SIFT characteristic based echinococcosis protoscolex survival rate detection method
  • Visual saliency and SIFT characteristic based echinococcosis protoscolex survival rate detection method

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Embodiment 1

[0015] Example 1, the method for detecting the survival rate of Echinococcus protoscoleum with the visual significance and SIFT features is carried out according to the following steps: In the first step, 20 to 100 Echinococcus protoscoleiae to be detected are passed through eosin Dyeing exclusion method or trypan blue staining treatment, after treatment, take pictures to obtain the processed image of Echinococcus protoscoleum to be detected; the second step is to extract the color and brightness image of the processed image of Echinococcus protoscoleum to be detected The saliency map; the third step is to linearly weight the saliency map of the color and brightness of the worm body image to generate the total saliency map; the fourth step is to extract the salient area of ​​the total saliency map, find the center point of the suspected worm body in the salient area and cut it For all suspected live insect slices, mark these suspected target areas in the salient areas, and then...

Embodiment 2

[0016] Example 2, as an optimization of the above-mentioned embodiment, the image data map of the live worm body of Echinococcus protoscoleum is obtained according to the following steps: first step, take 20 to 100 echinococcus protoscolums and undergo eosin rejection method or trypan blue staining treatment, after processing and taking pictures, to obtain the processed image of Echinococcus protoscoleum; in the second step, select 50 to 70 live insect images and corresponding The background image of the living insect body image builds a database; the third step is to extract the sift feature vectors of the live insect body image and the background image in the database through the SIFT algorithm; the fourth step is to cluster the sift feature vectors through the k-means algorithm, and then Put the clustered sift feature vectors into the svm classifier to obtain the image data map of live echinococcus protoscoleiae. The eosin rejection method, the trypan blue dyeing treatment,...

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Abstract

The invention relates to a visual saliency and SIFT characteristic based echinococcosis protoscolex survival rate detection method, and belongs to the technical field of detection methods. According to the visual saliency and SIFT characteristic based identification method, calculation is carried out on part of an image, only a suspected protoscolex area obtained from a saliency map is taken intoconsideration, a lot of time can be saved in the aspect of SIFT characteristic extraction, a k-means algorithm is used to cluster SIFT characteristic vectors of a sample image to change high-dimensioncharacteristic description of the SIFT characteristic vectors, the problems including too complex calculation and too much time consumption are solved, the calculating efficiency of target searchingvia the SIFT characteristics is improved, the SIFT characteristics in the salient area are more stable, and the problems that manual counting causes a manual error, low working frequency, long counting time and high workload are solved, and the correct rate of identification is ensured.

Description

technical field [0001] The invention relates to the technical field of detection methods, and relates to a method for detecting the survival rate of echinococcus protoscoleum with visual salience and SIFT features. Background technique [0002] Echinococcosis is a serious parasitic disease caused by the larvae of Echinococcus granulosus and Echinococcus multilocularis that affects both humans and animals and is most common in areas where livestock farming is common. China is one of the countries with high incidence of echinococcosis, including the Xinjiang Uygur Autonomous Region, and some areas are still showing a high prevalence trend. At present, in the process of echinococcosis prevention and control, the efficacy evaluation of echinococcosis drugs needs to be standardized, so that the efficacy evaluation and rational use need to be further improved. Among them, the in vitro determination of whether echinococcosis is dead is of great importance to the development of new ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V20/693G06V20/695G06V20/698G06V10/462G06F18/23213G06F18/2411
Inventor 吕国栋吕小毅李壮林仁勇刘辉库尔班尼沙·阿马洪
Owner THE FIRST TEACHING HOSPITAL OF XINJIANG MEDICAL UNIVERCITY
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