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Computer-implemented process on image of biological sample

A technology for biological samples and digital images, applied in computing, image analysis, image enhancement, etc., to solve problems such as unsatisfactory automatic classification

Pending Publication Date: 2021-02-26
VILNIUS UNIV +3
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, automatic classification of pixels (e.g. based on pixel color) is often unsatisfactory

Method used

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  • Computer-implemented process on image of biological sample
  • Computer-implemented process on image of biological sample
  • Computer-implemented process on image of biological sample

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0205] Figure 5 to Figure 16 A first example of the first embodiment of the present invention is shown. In this example, staining of tissue sections is performed using BIM.

[0206] Figure 5 A first digital image 103 (FIG. 1) is shown. Figure 6a Yes Figure 5 An enlarged view of a portion of the first digital image 103 . Figure 6b shown with Figure 6a The portion of the first digital image 103 shown in FIG. 1 corresponds to the portion of the second digital image 104 (FIG. 1).

[0207] Figure 7 is shown having as the first I 1 Gaussian curve 701 and the second I 1 The first histogram 601 of the fit of the sum of the Gaussian curve 702 ( Figure 4 ). Figure 8 is shown having as the first I 2 Gaussian curve 703 and the second I 2 The second histogram 602 of the fit of the sum of the Gaussian curve 704 ( Figure 4 ).

[0208] Figure 9a Shown is a picture 303 with point 705 submitted 403 to Organizational Scholar ( image 3 ). Figure 9b A picture 303 is s...

example 2

[0217] Figure 17 to Figure 28 A second example of the first embodiment of the present invention is shown. In this example, staining of tissue sections was performed using P-ERK.

[0218] Figure 17 A first digital image 103 (FIG. 1) is shown. Figure 18a Yes Figure 17 An enlarged view of a portion of the first digital image 103 . Figure 18b shown with Figure 18a The portion of the first digital image 103 shown in FIG. 1 corresponds to the portion of the second digital image 104 (FIG. 1).

[0219] Figure 19 is shown having as the first I 1 Gaussian curve 701 and the second I 1 The first histogram 601 of the fit of the sum of the Gaussian curve 702 ( Figure 4 ). Figure 20 is shown having as the first I 2 Gaussian curve 703 and the second I 2 The second histogram 602 of the fit of the sum of the Gaussian curve 704 ( Figure 4 ).

[0220] Figure 21a Shown is a picture 303 with point 705 submitted 403 to Organizational Scholar ( image 3 ). Figure 21b A pi...

example 3

[0229] Figure 29 to Figure 38 An example of the second embodiment of the present invention is shown. In this example, staining of the tissue section was performed using the three-color RGB3, where red corresponds to collagen fibers, cyan corresponds to cytoplasm, and violet corresponds to cell nuclei.

[0230] Figure 29 A first digital image 103 (FIG. 1) is shown. Figure 30a Yes Figure 29 An enlarged view of a portion of the first digital image 103 . Figure 30b shown with Figure 30a The portion of the first digital image 103 shown in FIG. 1 corresponds to the portion of the second digital image 104 (FIG. 1).

[0231] Figure 31a shown as I 1 and I 2 The two-dimensional distribution of the function of . The two-dimensional distribution forms a collection of one or more distributions. Each point 710 represents the value of a pixel in the second color space. Figure 31b shows the right Figure 31a Four Gaussian curves were fitted to the two-dimensional distribut...

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Abstract

The present invention relates to a computer-implemented process on an image of a biological sample. The present invention relates to the computer-implemented process to automatically analyze a digitalimage (103) of the biological sample (101). The process involves a change (203) from a first color space to a second color space, and then, fits are performed by taking into account several dimensions of the second color space to classify pixels.

Description

technical field [0001] The present invention relates to a computer-implemented method for classifying pixels of a digital image of a biological sample. Background technique [0002] Methods for automatic processing of images of biological samples are well known. However, automatic classification of pixels (eg, based on pixel color) is often unsatisfactory. For example, pixels located in regions that are actually undyed are often incorrectly classified as tinted. [0003] Literature MEAS-YEDID V et al.: "Automatic color space selection for biological image segmentation (automatic color space selection for biological image segmentation)", Pattern Recognition, 2004, ICPR2004, Proceedings of the 17th International Cambridge Conference, UK, August 2004 23 August-26 August, Piscataway, NJ, USA; IEEE, Los Alamitos, CA, USA, Volume 3, 23 August 2004 (2004-08-23), p. Pages 514-517, describe automatic color space selection for biological image segmentation. Contents of the invent...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/90
CPCG06T7/0012G06T7/90G06T2207/30024G06F16/55G06T7/60G06T2207/30004
Inventor L·普兰阿维达斯·劳里纳维修斯贝诺伊特·普朗科莱尼尼古拉斯·埃利
Owner VILNIUS UNIV