Dental Clinical Image Categorization for Automated Orientation
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Solution Overview
Problem
Existing methods for managing and processing clinical images in dentistry require manual pre-processing operations such as categorization and orientation adjustment, which are time-consuming and inefficient.
Innovation Solution
A system and method utilizing a categorization module and orientation modules to automatically categorize and adjust the orientation of clinical images, including facial and intra-oral images, by identifying feature points or areas and rotating images to align with reference lines, followed by cropping to a uniform size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual pre-processing operations are performed on clinical images, then categorization and orientation adjustment can be accomplished, but time consumption and inefficiency increase
Solution Approach 1:
The system enables self-service automation where the image processing system automatically performs categorization and orientation adjustment without requiring manual human intervention. The categorization module autonomously classifies images into predetermined categories, and orientation modules automatically adjust image orientations based on detected feature points and lines, eliminating the need for manual pre-processing operations and significantly reducing time consumption.
2Loss of information
If multiple clinical images are taken from different directions, then comprehensive diagnostic information is obtained, but the complexity of managing and processing these images increases
Solution Approach 1:
The system segments the complex image processing task into distinct functional modules: a categorization module that classifies images into predetermined categories (e.g., facial images, intra-oral images), and multiple orientation modules that handle different image types. This segmentation allows comprehensive diagnostic information from multiple image directions to be systematically organized and processed, reducing management complexity while preserving information completeness.
Solution Approach 2:
The system employs universal processing workflows that can handle multiple types of clinical images (facial images, intra-oral images, etc.) through a common framework. The categorization module directs images to appropriate orientation modules based on their type, and each module uses similar feature detection and orientation adjustment mechanisms, enabling the system to efficiently manage diverse images without requiring separate complex processing pipelines for each image type.
3Manufacturing precision
If manual orientation adjustment is performed on each clinical image, then precise alignment can be achieved, but productivity and processing efficiency decrease
Solution Approach 1:
The system replaces manual mechanical orientation adjustment with automated computational image processing. Orientation modules use computer vision algorithms to detect feature points and feature lines in images, calculate orientation angles relative to reference coordinate systems, and automatically rotate images to achieve precise alignment. This substitution of manual mechanical operations with automated computational methods maintains high precision while dramatically improving processing speed and productivity.
Solution Approach 2:
The system changes the parameter of orientation adjustment from manual control to automated computational determination. By detecting feature points and lines in images and calculating their orientation parameters relative to reference coordinate systems, the system automatically determines the required rotation angles and applies them, achieving precise alignment without manual intervention and enabling high-speed batch processing of multiple images.
Data Source
AI summary
A method for categorizing and adjusting an orientation of a clinical image is implemented using a system that includes a categorization module and a plurality of orientation modules. The method includes: in response to receipt of the clinical image, performing, by the categorization module, a categorization operation so as to categorize the clinical image into one of a plurality of predetermined categories; transmitting the clinical image to a corresponding one of the plurality of orientation modules based on a result of the categorization operation; and performing, by the corresponding one of the plurality of orientation modules, an orientation adjusting operation for adjusting the orientation of the clinical image. For a clinical image that is a facial image and for a clinical image that is an intra-oral image, different orientation adjusting operations may be performed, so as to generate an adjusted image that is properly oriented.