Ultrasonic Elasticity Image Classification via Statistical Parameters
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Solution Overview
Problem
The existing ultrasonic diagnostic apparatuses rely on visual estimation of elasticity images for determining the progress state of disorders, leading to variability among examiners, necessitating an objective method for classifying and estimating the progress of diseases based on image information.
Innovation Solution
An ultrasonic diagnostic apparatus equipped with ultrasonic wave transmitting/receiving means, tomogram constructing means, elasticity information calculating means, and display means, along with estimation data generating and classifying means to objectively estimate the progress state of disorders by analyzing RF signal frame data, calculating displacement and elasticity data, and generating classification results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual estimation of elasticity images is used to determine the progress state of disorders, then the method is simple and easy to implement, but the estimation results show variability among examiners leading to subjective and inconsistent diagnoses
Solution Approach 1:
The patent replaces the manual visual estimation process with an automated image processing system that objectively analyzes elasticity images. The system calculates distortion values, generates histograms, and automatically determines the progress state of disorders, eliminating examiner subjectivity while maintaining ease of operation through automated analysis.
Solution Approach 2:
The patent transforms the subjective visual estimation into objective quantitative parameters by calculating distortion values, generating histograms of elasticity data, and using statistical measures (mean, standard deviation, skewness, kurtosis) to characterize the elasticity image. This parameter transformation enables consistent and reproducible diagnosis.
2Measurement precision
If automated classification of elasticity images is implemented, then objective and consistent estimation results are achieved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent segments the elasticity image analysis into distinct processing stages: distortion calculation, histogram generation, statistical parameter extraction, and classification. This segmentation allows each function to be implemented independently using standard image processing techniques, managing complexity while achieving objective results.
Solution Approach 2:
The patent introduces intermediate processing steps including distortion calculation and histogram generation that bridge the gap between raw elasticity images and final classification. These intermediaries transform complex image data into simplified statistical parameters that are easier to process and interpret.
3Measurement precision
If multiple estimation parameters are calculated and analyzed, then the accuracy of disorder classification is improved, but the processing time and computational load increase
Solution Approach 1:
The patent calculates multiple statistical parameters (mean, standard deviation, skewness, kurtosis) from the elasticity histogram, but uses a subset of these parameters for the final classification decision. This partial action approach maintains high classification accuracy while reducing processing time compared to using all available parameters.
Solution Approach 2:
The patent transforms the elasticity image data into statistical parameters that capture the essential characteristics of the tissue in a compact form. By changing from pixel-level analysis to statistical parameter analysis, the system achieves high classification accuracy with reduced computational load.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The apparatus enables objective estimation and classification of the progress state of disorders, providing consistent and accurate results by analyzing RF signal frame data and generating estimation data for improved diagnostic accuracy.
Implementation Method 1
ultrasonic wave transmitting/receiving means for transmitting and receiving ultrasonic waves to/from an object
Implementation Method 2
reflection echo signal from the ultrasonic wave receiving means
Implementation Method 3
displacement of each point is measured by using a correlation calculation of ultrasonic wave reception signals of two frames which are adjacent to each other in time series
Implementation Method 4
This displacement is spatially differentiated to measure distortion
Data Source
AI summary
Ultrasonic diagnostic apparatus including: a tomogram constructing means; an elasticity information calculating means; an elasticity image constructing means; a display means; an estimation data generating means configured to generate estimation data for estimating the characteristic of a biomedical tissue on the basis of the elasticity image; an estimation data selecting means configured to select at least one parameter used for the estimation of the characteristic of the biomedical tissue and to make the selected parameter displayed on the display means, an analyzing means configured to analyze the elasticity image using at least one parameter selected by the estimation data selecting means and to make the result of analysis displayed on the display means; wherein the display means displays the parameter used for the estimation of the characteristic and the result of analysis.