Medical Image Property Change Matrix for Interstitial Pneumonia
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Solution Overview
Problem
Existing methods for analyzing changes in medical images over time, such as those for interstitial pneumonia, primarily emphasize differences between images, making it difficult to accurately determine changes in specific properties between images taken at different times.
Innovation Solution
An image processing device that classifies each pixel in target regions of medical images taken at different times into various properties using a discriminative model, and then derives quantitative values for property pairs representing changes between corresponding pixels in those regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only the difference between images is emphasized and displayed, then visual recognition of changes is improved, but accurate identification of specific property changes is lost
Solution Approach 1:
The patent segments the property change information by creating a matrix that divides property changes into specific categories (e.g., normal to abnormal, abnormal to normal, abnormal to abnormal transitions). This segmentation allows the system to present detailed property change information in an organized, easily comprehensible format while maintaining visual emphasis on overall changes through color-coded representations.
Solution Approach 2:
The patent applies color changes to different property transitions in the matrix, where different colors represent different types of property changes (e.g., improvement, deterioration, no change). This visual encoding maintains ease of recognition while preserving specific property information, as each color corresponds to a specific property transition type that can be interpreted by clinicians.
2Measurement precision
If multiple properties are classified for each pixel, then diagnostic accuracy is improved, but complexity of analysis increases
Solution Approach 1:
The patent merges multiple property classification results into a unified property change matrix that displays all property transitions in a single comprehensive view. Instead of analyzing each property separately, the system combines multi-property classification data and presents it as an integrated matrix showing all property transitions simultaneously, reducing analysis complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The property change matrix serves multiple diagnostic functions simultaneously: it identifies disease progression, tracks treatment response, detects new lesions, and monitors regression across multiple tissue properties. This multi-functional approach allows the system to handle complex multi-property classification results through a single versatile tool that addresses various diagnostic needs without increasing operational complexity.
Data Source
AI summary
An image processing device, an image processing method, and an image processing program make it possible to accurately recognize property changes between medical images having different imaging times. A property classification unit classifies each pixel included in a target region of each of a first medical image and a second medical image of which an imaging time is later than an imaging time of the first medical image for the same subject into any one of a plurality of types of properties. A quantitative value derivation unit specifies property pairs each representing a property change between corresponding pixels in at least partial regions within the target regions, and derives a quantitative value in the at least partial regions for each of the property pairs.