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Image quantitative analysis method

An analysis method and image quantification technology, applied in the field of image processing and machine learning applications, can solve problems such as limited resolution and inability to achieve human analysis, and achieve the effect of intuitive abnormal areas, intuitive discrimination, and enhancement of abnormal areas

Inactive Publication Date: 2018-10-26
SHANGHAI SIXTH PEOPLES HOSPITAL
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Usually, the resolution of images collected by equipment is limited, which cannot meet the requirements of data analysis or human analysis. Even if the resolution of images collected by some existing high-end equipment is very high, it cannot meet the requirements of human analysis. , so the collected high-resolution images must be further processed to meet further analysis requirements

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  • Image quantitative analysis method
  • Image quantitative analysis method

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Experimental program
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Embodiment 1

[0022] Embodiment 1: as figure 1 Shown, a kind of image quantitative analysis method comprises step 01)-step 02):

[0023] Step 01) Acquire raw image samples. In this step 01), the original image samples include original training samples and original test samples. To meet the needs of the following steps, in this embodiment, the original training samples are divided into training set samples and verification set samples. For example, the original image samples used in this embodiment are MRI images of T1 and PD modalities, the image resolution is 0.3mm×0.3mm×1mm, and the image size is 480mm×480mm×24mm. The data precision of the original training sample and the original test sample is adjusted to single and stored in .hdf5 format. The original training samples can be MRI images of T1 and PD modalities of healthy people. Characteristic regions of the cartilage-bone joint of the knee in this MRI image.

[0024]Step 02) Smooth blurring. Lesion area smooth blur is used to blu...

Embodiment 2

[0029] Embodiment 2. The difference between this embodiment and Embodiment 1 is that the smoothing and blurring processing steps adopted in this embodiment are different from those in Embodiment 1. Specifically, this embodiment adopts step 202 to describe this in detail:

[0030] Step 022): In the smoothing and blurring processing step, normalization processing is included: the mat2gray function in MATLAB is used to carry out normalization processing to the original image sample, so that the output image data interval is set to [0,1]; in this original The center of the image sample acquires a preset size area, and the pixel average value of the preset size area is used as the pixel value of the original image sample.

[0031] Based on the training samples processed in step 202 above, the subsequent steps are also different. For example, in step 03), the super-resolution reconstruction network structure can be shared, but the respective network parameters need to be trained sep...

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Abstract

The invention discloses an image quantitative analysis method. The method comprises the following steps of obtaining an original image sample, wherein the original image sample includes an original training sample and an original test sample; performing smooth fuzzy processing, normalizing image block data in the original training sample and the original test sample to reduce the resolution of theimage for obtaining a fuzzy training sample and a fuzzy test sample, and enabling the fuzzy test sample to have the same data features as the fuzzy training sample; and training a super-resolution reconstruction model, processing the fuzzy test sample, and constructing a residual error spectrum, wherein a high-gray value response region in the residual error spectrum is namely an abnormal featureregion.

Description

technical field [0001] The invention relates to the fields of machine learning applications, image processing, etc., and specifically relates to an image quantification analysis method, especially a processing method for original high-resolution images. Background technique [0002] Machine learning (Machine Learning, ML) is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines, specializing in the study of how computers simulate or realize the learning behavior of human groups , to acquire new knowledge or skills, and reorganize the existing knowledge structure to continuously improve its own performance. Machine learning is the core of artificial intelligence and can be applied to many fields such as data mining, computer vision, natural language processing, biometric identification, search engines, medical diagnosis, detection of credit card fraud, securit...

Claims

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Application Information

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IPC IPC(8): G06N99/00G06N3/04G06K9/62
CPCG06N3/045G06F18/214
Inventor 姚伟武王乾刘成磊
Owner SHANGHAI SIXTH PEOPLES HOSPITAL
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