Radiograph Classification Using Invariant Shape Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for automated classification of radiographic images are inefficient due to their reliance on manual input, limited use of image semantics, and inability to handle rotation and translation variance, leading to biased classification results and high observer dependency.
Innovation Solution
An automated method for classifying radiographs based on the physical size and shape of anatomical structures, using image segmentation to distinguish between foreground, background, and anatomy regions, and employing a scale, rotation, and translation invariant shape classifier to accurately categorize images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated classification methods are implemented, then productivity is improved, but measurement precision deteriorates due to inability to handle rotation and translation variance
Solution Approach 1:
The patent transforms the radiograph image parameters by applying geometric transformations (rotation and translation) to generate multiple views of the same anatomy. This allows the classification system to recognize anatomical structures regardless of their orientation or position in the original image, thereby maintaining high classification accuracy while enabling automated processing.
Solution Approach 2:
The patent performs preliminary geometric transformations and generates multiple transformed views of the radiograph before classification. By pre-processing the images to account for all possible rotations and translations, the system eliminates the need for manual orientation correction and achieves both automation and precision in classification.
2Measurement precision
If manual input methods are used for image classification, then measurement precision is improved through expert judgment, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The patent enables the classification system to automatically perform tasks that previously required manual expert intervention. By implementing automated geometric transformation and classification algorithms, the system serves itself by correctly identifying and classifying anatomical structures without human input, thereby achieving both high productivity and maintained precision through sophisticated image processing.
3Device complexity
If traditional image classification methods are used, then device complexity is reduced, but loss of information increases due to inability to capture anatomy semantics
Solution Approach 1:
The patent segments the radiograph image into multiple transformed views, each representing the same anatomy from different geometric perspectives. This segmentation allows the system to capture comprehensive anatomical information while maintaining manageable system complexity through modular processing of individual transformed images.
Data Source
AI summary
A method for automatically classifying a radiograph. A digital radiographic image is acquired, wherein the image is comprised of a matrix of rows and columns of pixels. The digital image is segmented into foreground, background, and anatomy regions. A physical size of the anatomy region is classified. An edge direction histogram of the anatomy region is generated and a shape pattern of the edge direction histogram is classified. Based on the physical size classification and the shape pattern classification, the image is categorized.