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Image analysis and identification method for lateral flow scrip disease diagnosis

A disease diagnosis and image analysis technology, applied in the field of image processing, can solve problems such as inability to save, misjudgment, difficulty in ensuring consistency and repeatability of test results, etc., to achieve the effect of solving subjectivity and ensuring accuracy

Active Publication Date: 2018-07-24
BEIJING UNIV OF CHEM TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

There are certain inconveniences in the operation of this method, such as the inability to form an electronic medical record and the inability to save it; especially, affected by the subjective factors of the operator and the interference of the detection environment (such as ambient light intensity), weak positive detection with light color The results may be misjudged, and the consistency and repeatability of the test results are difficult to guarantee; at the same time, this detection method that relies on human eye observation can only achieve qualitative detection

Method used

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  • Image analysis and identification method for lateral flow scrip disease diagnosis
  • Image analysis and identification method for lateral flow scrip disease diagnosis
  • Image analysis and identification method for lateral flow scrip disease diagnosis

Examples

Experimental program
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Embodiment 1

[0043] Step 1: Image Preprocessing

[0044] Obtain a color image, segment the target detection area, grayscale the image, use the median filter to denoise the image, and use the Gaussian difference pyramid to perform image photometric homogenization processing in the case of uneven image luminosity;

[0045] Step 2: Build the "ten" character template

[0046] Construct figure 2 For the "ten" template shown, the number i of X-axis pixels of the template is roughly half of the number of pixels on the X-axis of the detection area, here it is 12, and the number of pixels j on the Y-axis is roughly the number of pixels on the Y-axis of the detection area half of , here is 29;

[0047] Step 3: Use the "ten" template to retrieve the lowest pixel value point within the image range

[0048] Use the "ten" template to calculate the average pixel value of each pixel in the target detection area, and record the coordinates of the minimum pixel value point (X min ,Y min ), record the ...

Embodiment 2

[0066] Step 1: Image Preprocessing

[0067] Obtain a color image, segment the target detection area, grayscale the image, use the median filter to denoise the image, and use the Gaussian difference pyramid to perform image photometric homogenization processing in the case of uneven image luminosity;

[0068] Step 2: Build the "ten" character template

[0069] Construct figure 2 For the "ten" template shown, the number i of X-axis pixels of the template is approximately half of the number of pixels on the X-axis of the detection area, here it is 11, and the number of pixels j on the Y-axis is approximately the number of pixels on the Y-axis of the detection area half of , here is 27;

[0070] Step 3: Use the "ten" template to retrieve the highest pixel value point within the image range

[0071] Use the "ten" template to calculate the average pixel value of each pixel in the target detection area, and record the coordinates of the point with the maximum pixel value (X max ...

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Abstract

The invention discloses an image analysis and identification method for lateral flow scrip disease diagnosis; the method comprises the following steps: obtaining an image grey-scale map, and preprocessing same to realize image denoising and luminosity uniformization; using a cross-type template to search maximum or minimum pixel value points, using a threshold method and an edge detection method to search top and bottom boundaries of the detection area according to the searched maximum / minimum value pixel point, and multi-verifying the difference value between the top and bottom boundaries; determining the top and bottom boundaries, setting a certain scope in the left and right sides of the pixel value maximum / minimum value point, using the edge detection method to search the left and right boundaries of a control line, and verifying the width difference between the left and right boundaries; based on the control line position, using a gradient method, a max-min value method and the edge detection method to search the left and right boundaries of a test line in sequence, and multi-determining the reasonability of the searching result. The method can multi-search and verify to-be-identified detection areas, thus improving the analysis and identification precision.

Description

technical field [0001] The invention relates to an image analysis and recognition method applied to the diagnosis of lateral flow paper strip diseases, in particular to a search and recognition algorithm in the field of image processing. Background technique [0002] With the continuous improvement of the level of medical care, disease diagnosis that can realize on-site rapid detection has attracted more and more attention. The disease detection method based on the lateral flow paper strip has been playing a huge role in disease diagnosis, medical care and prevention because of its advantages of accurate results, simple operation, convenience, short detection time, and low detection cost. The invention proposes an image processing and analysis method for disease diagnosis of laterally flowing paper strips, and realizes automatic and intelligent detection of laterally flowing paper strips. [0003] The interpretation of the detection results of lateral flow paper strips, suc...

Claims

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Application Information

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IPC IPC(8): G16H50/20G06T7/13
CPCG06T7/13
Inventor 邱宪波牛亚男纪银环
Owner BEIJING UNIV OF CHEM TECH
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