Kidney Region Detection via Principal Component Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated methods fail to accurately and automatically detect kidney regions, particularly the cortical region, in dynamic studies, which are crucial for renal function evaluation, and require significant expertise and are time-consuming.
Innovation Solution
A method and system for automatic detection of kidney regions involving identifying regions of interest, extracting separate dynamic sequences, and performing principal component analysis on each sequence, with linear combination of the first few component images using normalization coefficients based on absolute values to enhance image clarity and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual detection methods are used for kidney regions, then detection accuracy can be maintained, but time consumption and operator expertise requirements increase significantly
Solution Approach 1:
The system performs automatic detection of kidney regions and cortical areas without requiring manual operator intervention. The algorithm independently identifies anatomical structures, extracts dynamic sequences, performs PCA analysis, and segments regions of interest, enabling the system to serve itself and eliminate time-consuming manual operations while maintaining detection accuracy
Solution Approach 2:
The patent replaces manual mechanical detection operations with an automated computational system. The algorithm substitutes human operator actions with computer-based image processing, including automatic region identification, dynamic sequence extraction, principal component analysis, and automated segmentation, thereby reducing time consumption while preserving measurement precision
2Productivity
If existing automated methods are used for kidney region detection, then time consumption is reduced, but detection accuracy and reliability deteriorate
Solution Approach 1:
The patent segments the detection process into distinct automated stages: identifying overall kidney regions, extracting separate dynamic sequences for each kidney, performing PCA analysis on individual sequences, and segmenting cortical regions. This multi-stage segmentation approach enables accurate automated detection by breaking down the complex task into manageable, precise sub-tasks that maintain high detection accuracy while achieving full automation
Solution Approach 2:
The system performs preliminary actions by first identifying kidney regions and extracting dynamic sequences before performing the main detection task. The PCA analysis is performed on pre-processed individual kidney sequences, preparing the data in advance for accurate cortical region segmentation. These preliminary automated steps ensure detection accuracy is maintained while achieving productivity improvement
3Reliability
If manual region identification is performed, then expertise requirements are met, but operator dependency increases and productivity decreases
Solution Approach 1:
The automated system performs all detection operations independently without requiring operator expertise or intervention. The algorithm self-manages the entire workflow from region identification to cortical segmentation, eliminating operator dependency while maintaining reliable detection results through robust image processing and analysis algorithms
4Measurement precision
If separate dynamic sequences are extracted and PCA is performed on each kidney, then detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the processing into separate operations for each kidney: extracting individual dynamic sequences, performing PCA analysis on each sequence separately, and independently segmenting cortical regions. This segmentation improves detection accuracy by treating each kidney independently, while the modular structure manages complexity through systematic organization of processing steps
Data Source
AI summary
In some preferred embodiments, a system for the automatic identification of cortical and/or medulla regions of the kidneys in renal dynamic studies is provided that includes: a computer module configured to perform principal component analysis on a dynamic sequence corresponding to only one of the kidneys based on a linear combination of the first few component images with coefficients for the principal component analysis as normalization factors.