Anatomical Element Detection Using Decision Tree Classifiers
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Solution Overview
Problem
Manual labeling of anatomical elements in medical images is time-consuming and prone to errors, delaying patient results and reducing practitioner availability.
Innovation Solution
A method and apparatus that utilize an image classifier and anatomical model to automatically detect and label anatomical elements in medical images by generating a classified image and applying decision trees to determine pixel probabilities associated with anatomical features, using Haar-like features and anatomical data to optimize decision tree accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of anatomical elements is performed, then accuracy of anatomical element identification is improved, but time consumption and practitioner workload increase significantly
Solution Approach 1:
The system performs preliminary classification of image regions using decision trees trained on anatomical features, generating a classified image that pre-identifies potential anatomical element locations before final detection. This preliminary action reduces the time required for complete anatomical element identification while maintaining accuracy through subsequent refinement using anatomical models.
2Measurement precision
If manual labeling of anatomical elements is performed, then accuracy of anatomical element identification is improved, but practitioner availability for other tasks is reduced
Solution Approach 1:
The system enables self-service by automatically performing anatomical element detection and labeling without requiring practitioner intervention for the labeling process. The decision tree classifier and anatomical models work autonomously to identify and label anatomical elements, freeing practitioners to focus on higher-value diagnostic and patient care tasks while maintaining identification accuracy.
3Productivity
If automated detection using decision trees is implemented, then time consumption is reduced, but detection accuracy may be compromised
Solution Approach 1:
The system introduces an anatomical model as an intermediary between the decision tree classification and final anatomical element detection. The classified image from decision trees serves as input to the anatomical model, which refines the detection by applying anatomical knowledge and constraints. This intermediary step ensures that automated detection maintains high accuracy while benefiting from the speed of algorithmic processing.
4Measurement precision
If complex image classification is performed to improve detection accuracy, then detection precision is improved, but computational complexity increases
Solution Approach 1:
The system segments the detection process into distinct stages: feature extraction, decision tree classification to generate a classified image, and anatomical model-based refinement. Each stage handles a specific aspect of the detection task, reducing the computational complexity of any single step while maintaining overall detection precision through the coordinated sequence of simpler operations.
Data Source
AI summary
A method, apparatus and computer program product are hereby provided to detect anatomical elements in a medical image. In this regard, the method, apparatus, and computer program product may receive a test image and generate a classified image by applying an image classifier to the test image. The image classifier may include at least one decision tree for evaluating at least one pixel value of the test image and the classified image may include a plurality of pixel values. Each pixel value may be associated with a probability that an anatomical element is located at the pixel location. The method, apparatus, and computer program product may also evaluate the classified image using an anatomical model to detect at least one anatomical element within the classified image.