Lesion Discrimination via Intensity Step Contour Analysis
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Solution Overview
Problem
Existing medical imaging technologies face challenges in distinguishing between benign and malignant lesions in MRI images due to transformations that obscure original signal strength, making it difficult for radiologists to accurately evaluate lesions based on intensity and spatial relationships.
Innovation Solution
A method that determines the locations of pixels indicating abnormalities, constructs contours around these pixels at various intensity levels, computes characteristic values, and uses threshold values to discriminate between benign, cancerous, or uncertain lesions by analyzing changes in these values across intensity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If linear transformations (windowing/leveling) are applied to image intensity to optimize display, then the medically relevant portions of the image are shown in the central portion of the displayed intensity scale, but the original source signal strength information is obscured and difficult to compare
Solution Approach 1:
The patent introduces an intermediary computational process that operates on the transformed image data to recover and analyze original signal strength characteristics. By computing intensity steps through contour analysis at multiple threshold levels, the system mediates between the displayed transformed data and the original signal information, allowing both display optimization and signal strength discrimination to coexist
Solution Approach 2:
The patent transforms the problem from analyzing raw intensity values to analyzing the dimensional changes in contour characteristics across multiple intensity thresholds. By examining how contour area, perimeter, and shape metrics change across intensity levels, the system extracts signal strength information in a new dimensional space that is independent of the display transformation
2Reliability
If multiple time frames and extensive image processing are used to improve diagnostic accuracy, then more diagnostic information can be obtained, but the complexity and time required for analysis increases
Solution Approach 1:
The patent extracts the essential diagnostic information by focusing solely on intensity step analysis from a single image, eliminating the need for multiple time frames. By taking out only the critical feature (intensity step count) from the complex imaging data, the system achieves reliable diagnosis with minimal input requirements and processing complexity
Solution Approach 2:
The patent uses partial action by analyzing only the intensity step characteristics rather than performing extensive processing of all image features. This selective approach achieves diagnostic accuracy by focusing on the most discriminating feature while avoiding unnecessary processing steps
3Ease of operation
If radiologists manually evaluate lesion intensity and spatial relationships, then diagnostic decisions can be made, but the evaluation is difficult and subject to error due to transformed intensity values
Solution Approach 1:
The patent enables the image data to serve itself by automatically computing intensity steps and generating diagnostic recommendations without requiring manual radiologist interpretation of transformed intensities. The system uses self-service computational algorithms to extract and analyze the critical features, eliminating human error in intensity evaluation while preserving diagnostic capability
Data Source
AI summary
A method of identifying the location of a lesion in an image and evaluating whether the identified lesion is more likely to be cancerous, benign or uncertain is provided where the image includes a plurality of pixels, each pixel having a particular intensity I in the range of 0<I<2<sup.