OCT Image Normalization for Medical Information Quality
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Solution Overview
Problem
Conventional technologies using mathematical models trained by machine learning algorithms often fail to produce high-quality medical information when input OCT images have characteristics different from the training images.
Innovation Solution
An OCT image processing device that performs a normalization process by approximating the characteristics of the OCT image to the statistical information of the training images, generating a normalized image that is then input into the mathematical model for enhanced medical information acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a mathematical model trained by machine learning algorithm is used to process OCT images, then medical information can be acquired automatically, but the quality of medical information deteriorates when the input OCT image has characteristics different from training images
Solution Approach 1:
The patent applies preliminary action by performing normalization processing on the input OCT image before it is fed into the mathematical model. The normalization unit adjusts the characteristics of the input image to match the statistical characteristics of the training images used during model training. This preprocessing step ensures that the input image conforms to the expected distribution, thereby maintaining high-quality medical information output even when the original input image has different characteristics from the training data.
2Manufacturing precision
If normalization processing is performed to align OCT image characteristics with training images, then the quality of medical information output is improved, but the processing complexity increases
Solution Approach 1:
The patent implements parameter changes by adjusting the statistical parameters of the input OCT image during normalization processing. The normalization unit calculates statistical characteristics (such as mean and standard deviation) from the training images and transforms the input image parameters to match these statistics. This approach improves medical information quality by ensuring parameter alignment between input and training images, while the automated parameter adjustment keeps the added complexity manageable.
Data Source
AI summary
An OCT image processing device includes a control unit is programmed to perform: an image acquisition step of acquiring the OCT image taken by the OCT device; a normalization step of performing a normalization process on the OCT image acquired at the image acquisition step to generate a normalized image from the OCT image; and a medical information acquisition step of acquiring medical information output by a mathematical model by inputting the normalized image generated at the normalization step into the mathematical model that has been trained by a machine learning algorithm using a plurality of training images that are OCT images. The normalized image is generated by approximating at least one of characteristics of the OCT image to statistical information of characteristics of the plurality of training images.