Medical Image Alignment and Difference Analysis for Clinical Change Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for diagnosing diseases using medical images are time-consuming, prone to human error, and lack standardization, especially when analyzing temporal changes in patient images, which can lead to inaccurate classification due to heterogeneity in patient populations.
Innovation Solution
The system aligns prior medical images with current images through auto-scaling and registration, generates difference images based on pixel intensity, and applies these to a machine learning model to detect changes over time, using deep learning neural nets to classify images as normal or cancerous, thereby improving accuracy and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual image analysis methods are used, then diagnostic accuracy may be maintained through human expertise, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The patent introduces an automated image analysis system that acts as an intermediary between medical images and diagnostic conclusions. The system uses computer algorithms to preprocess, register, and analyze medical images, providing consistent and rapid diagnostic support while reducing human error and variability in interpretation
Solution Approach 2:
The patent replaces manual mechanical analysis methods with automated computational systems. Traditional manual image review by radiologists is substituted with algorithm-based image processing, registration, and analysis pipelines that can process images faster and with greater consistency, while maintaining or improving diagnostic accuracy
2Productivity
If automated machine learning classification is applied, then analysis time is reduced and consistency is improved, but accuracy deteriorates due to heterogeneity in patient populations
Solution Approach 1:
The patent applies preliminary image processing steps including auto-scaling and registration before classification. These preliminary actions standardize the input images by aligning them to a common reference frame and adjusting for variations in imaging parameters, thereby improving the accuracy of subsequent machine learning classification while maintaining fast processing speeds
Solution Approach 2:
The patent transforms images into a standardized parameter space through registration and scaling operations. By changing the spatial and intensity parameters of input images to match a common reference, the system reduces heterogeneity effects and improves classification accuracy across diverse patient populations while maintaining automated processing efficiency
3Measurement precision
If image registration and alignment procedures are performed, then classification accuracy is improved through better feature alignment, but the complexity of the system increases
Solution Approach 1:
The patent employs self-service registration techniques where the system automatically performs alignment and scaling operations without requiring manual intervention. The automated registration algorithms independently identify anatomical landmarks and adjust image parameters, reducing system complexity from a manual operation perspective while maintaining high alignment accuracy for improved classification
Data Source
AI summary
Methods, systems, and computer readable media are provided for processing medical images. One or more prior medical images are aligned with a current medical image. Image subtraction between the current medical image and the one or more prior medical images is performed to produce one or more difference images. The one or more difference images are applied to a machine learning model to determine a presence or an absence of a medical condition.