Medical Image Processing Apparatus for Local Lung Disease State Change Detection
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Solution Overview
Problem
Conventional medical image processing techniques fail to accurately assess changes in the disease state of diffuse pulmonary diseases in local lung regions, relying on subjective evaluations and lacking objective methods to determine whether the disease is in a recovery or exacerbation phase.
Innovation Solution
A medical image processing apparatus that acquires and analyzes X-ray CT images over time, classifies tissue properties into texture patterns, and uses a lookup table to estimate disease state changes, generating a disease-state-change map to objectively determine recovery or exacerbation directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional texture analysis is performed on pulmonary X-ray CT images to classify lung field into texture patterns and display volume ratios, then the overall disease distribution can be visualized, but the change in disease state in local regions cannot be grasped
Solution Approach 1:
The patent segments the lung field into multiple local regions and performs texture analysis on each region independently. By dividing the overall lung field into smaller units, the system can assess both the global disease distribution (volume ratios) and local disease state changes simultaneously, resolving the contradiction between comprehensive coverage and local precision.
2Quantity of substance
If conventional techniques display volume ratio of each texture pattern to entire lung field, then overall disease burden is quantified, but the direction of disease progression (recovery or exacerbation) cannot be determined
Solution Approach 1:
The patent performs preliminary classification of texture patterns into categories indicating different disease states (normal, ground-glass opacity, consolidation, etc.) before temporal comparison. By pre-establishing this classification framework, the system can not only quantify current disease burden but also track transitions between states over time to determine progression direction.
Solution Approach 2:
The system implements feedback by comparing texture pattern classifications across multiple time points and using this temporal information to infer disease progression direction. The feedback loop allows the system to distinguish between recovery (transition from abnormal to normal patterns) and exacerbation (transition from normal to abnormal patterns), preserving temporal progression information.
3Adaptability or versatility
If subjective evaluation is used for disease assessment, then clinical judgment can be applied, but objective measurement of disease state change is lacking
Solution Approach 1:
The patent introduces texture pattern classification as an intermediary between subjective clinical evaluation and objective image data. The system automatically classifies regions into standardized texture categories based on image features, providing an objective bridge that maintains clinical interpretability while eliminating subjective variability in disease state assessment.
Data Source
AI summary
In one embodiment, a medical image processing apparatus includes a memory storing a predetermined program and processing circuitry. The processing circuitry is configured, by executing the predetermined program, to acquire a plurality of images that are obtained by imaging a same object and are different in imaging time, classify tissue property of the object into a plurality of tissue-property classes by analyzing the tissue property of the object based on pixel values of respective regions of the plurality of images, assign the classified tissue-property classes to the respective regions of the plurality of images, and estimate change in disease state of the object from change in the classified tissue-property classes in respectively corresponding regions of the plurality of images.