Medical Image Texture Analysis for Cross-Device Comparison
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Solution Overview
Problem
Comparing medical images generated by different scanning devices and at various times is challenging due to differences in scanning methods and devices, leading to false positives and negatives, and requires domain-specific knowledge, which is difficult to access and prone to human error.
Innovation Solution
A computer-implemented method that retrieves and compares texture metrics from medical images using texture analysis, identifying areas of interest and applying correction factors based on scanning characteristics to output a change metric, enabling accurate and robust comparison of medical images across different scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical images are compared by sight by radiologists, then domain-specific knowledge is utilized, but the process is time-consuming and requires access to highly trained individuals which creates a bottleneck
Solution Approach 1:
The patent replaces the manual visual comparison process performed by radiologists with an automated computer-implemented texture analysis system. The system uses texture metrics and image processing algorithms to objectively compare medical images, substituting human expert analysis with computational methods that can process images rapidly without requiring access to specialized medical professionals.
2Adaptability or versatility
If medical images from different scanning devices and locations are compared, then more data becomes available, but differences in scanning methods and devices lead to false positive and false negative errors
Solution Approach 1:
The patent applies correction factors based on scanning characteristics to normalize images from different devices and scanning methods. By adjusting parameters such as texture metrics and image features according to device-specific characteristics, the system compensates for variations introduced by different scanners, locations, and scanning protocols, enabling reliable comparison across heterogeneous data sources.
3Device complexity
If traditional image comparison methods are used, then simplicity is maintained, but the ability to detect subtle changes is limited and results are qualitative rather than quantitative
Solution Approach 1:
The patent divides the medical image into regions of interest and applies texture analysis to specific anatomical structures. By segmenting the image and focusing analysis on relevant areas, the system maintains operational simplicity while dramatically improving the precision of change detection through quantitative texture metrics.
Solution Approach 2:
The patent transitions from qualitative visual assessment to quantitative measurement by introducing texture metrics as an additional dimension of analysis. This allows subtle changes in anatomical structure to be detected through numerical values and statistical comparisons, providing objective and precise measurements beyond what is visible through simple visual inspection.
Data Source
AI summary
An embodiment of the invention relates to a scanning device. The scanning device includes a scanning unit to detect radiation received during a scanning operation on an object. An imaging unit is arranged to reconstruct an image for a location on the object based on the detected radiation. A texture analysis unit receives an indicated area of interest of a medical image and computes at least one texture metric for the area of interest. An image comparison unit receives a plurality of texture metrics for a common area of interest within respective medical images and outputs a change metric indicating a measure of variation over time for the area of interest based on a comparison of the plurality of texture metrics.