Microscopic Image Annotation with Proximity-Based Region Detection
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Solution Overview
Problem
Existing image annotation systems for microscopic images are inefficient and require precise user input to define image areas, especially when objects or regions touch each other, hindering fast and easy annotation for training datasets.
Innovation Solution
An image annotation system that allows users to define first and second closed paths with a cursor, determining image areas based on proximity conditions, automatically adding regions between borders when necessary, and providing intuitive input methods like buttons or touchscreens for efficient annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users define image areas by precisely tracing borders with cursor movements, then annotation accuracy is improved, but annotation speed and ease of operation deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically detecting object borders and pre-defining image areas before user confirmation. The processor identifies borders in the microscopic image and creates preliminary image area definitions that users can accept with minimal input, eliminating the need for users to manually trace every border while maintaining accurate annotations.
2Manufacturing precision
If users manually trace precise borders of image areas using cursor movements, then boundary definition accuracy is improved, but operation complexity and time consumption increase
Solution Approach 1:
The system performs self-service by automatically detecting borders and defining image areas without requiring precise manual user input. The processor autonomously identifies object boundaries in the microscopic image and creates accurate image area definitions, allowing users to simply confirm the automatically generated annotations rather than manually tracing borders.
Solution Approach 2:
The patent replaces the mechanical system of manual cursor tracing with an automated image processing system. Instead of requiring users to physically move a cursor along borders, the system uses automated border detection algorithms that analyze pixel intensity gradients and other image features to precisely identify object boundaries and define image areas.
3Measurement precision
If the system requires precise cursor movements to define image areas, then annotation accuracy is improved, but throughput and efficiency deteriorate
Solution Approach 1:
The system replaces manual cursor tracing mechanics with automated image processing. The processor uses algorithms to detect borders by analyzing pixel intensity changes and automatically defines image areas, eliminating the time-consuming manual tracing process while maintaining high annotation accuracy through sophisticated border detection.
Solution Approach 2:
The system changes the parameter of border definition from manual coordinate specification to automated image feature analysis. By transforming the annotation process from requiring precise cursor positioning to using automated border detection based on image intensity gradients and other parameters, the system achieves both high accuracy and high throughput simultaneously.
Data Source
AI summary
An image annotation system for annotating a microscopic image of a sample includes an output unit configured to display the image of the sample, and an input unit configured to capture a first and second user input sequence including a first and second movement of a cursor over the image, the first and second movement defining a first and second closed path. The image annotation system also includes a processor configured to determine a first image area, determine whether the second closed path includes at least one proximity section, when the second closed path does not include the proximity section, to determine a second image area, and when the second closed path does include the proximity section, to determine the second image area and at least one area between the border of the first image area and the at least one proximity section.