A Machine Learning-Based Automatic Focusing Method for Optical Microscope

An optical microscope and machine learning technology, applied in the field of medical image processing, can solve the problems of slow speed and poor versatility, and achieve the effects of avoiding return error, improving focusing speed, and ensuring focusing accuracy

Active Publication Date: 2021-07-06
湖南品信生物工程有限公司
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

A large number of search strategies such as hill climbing method, dichotomy method, Fibonacci search method, fuzzy control search method, adaptive step size method, function curve fitting method, discrete difference equation prediction method, etc. have been used to automatically focus, to a certain extent Improves the speed and precision of focusing, but has the disadvantages of poor versatility and slow speed, and is not suitable for automatic focusing of microscopes with high precision

Method used

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  • A Machine Learning-Based Automatic Focusing Method for Optical Microscope
  • A Machine Learning-Based Automatic Focusing Method for Optical Microscope
  • A Machine Learning-Based Automatic Focusing Method for Optical Microscope

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Embodiment Construction

[0113] In order to make the objectives, technical solutions and beneficial technical effects of the present invention clearer, the present invention will be further described in detail below in conjunction with examples. It should be understood that the embodiments described in this specification are only for explaining the present invention, not for limiting the present invention, and the specific parameter settings of the embodiments can be selected according to local conditions and have no substantial impact on the results.

[0114] Step 1: If figure 2 As shown, 200 raw images were collected along the Z-axis of the optical microscope I i (c, r), and converted to a grayscale image f i (c, r), where i = {1, 2, ..., p};

[0115] Step 2: Repeat step 1, each time as a group (such as figure 2 shown), collecting 20 sets of data and a total of N pictures;

[0116] Step 3: Calculate 48 original features for each image and 96 combined features and respectively represent the...

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Abstract

The invention proposes an automatic focusing method of an optical microscope based on machine learning, which belongs to the technical field of medical image processing. In this method, the pictures collected and grouped by the optical microscope are represented by the designed original features and combined features, and the sequence difference between the picture and the clearest picture in the group is used as the label of the picture, and then a random forest composed of a regression tree is used to The importance of the original features and combined features is calculated, combined with the set threshold multiple iterations to filter out the features with higher importance, and then use the leave-one-out method and the filtered features to divide the data into training set and test set to train the gradient The regression tree is improved, and the strong regressor obtained by iterative training is finally automatically focused.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and relates to an automatic focusing method of an optical microscope based on machine learning. [0002] technical background [0003] The traditional method of manual image reading has brought heavy labor to pathologists, and reading images for a long time with high concentration is prone to visual fatigue, which greatly increases the probability of misdiagnosis. In recent years, with the development of automation and intelligence of microscopes, automatic reading technology has begun to appear and develop rapidly. The automatic film reading technology of the microscope uses the automatic focusing algorithm to capture clear images under the microscope, and then conducts subsequent pathological analysis. As the first step of the automatic film reading technology, the automatic focusing algorithm of the microscope greatly affects the subsequent pathological analysis process, and it...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G02B21/24
CPCG02B21/244
Inventor 梁毅雄
Owner 湖南品信生物工程有限公司
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