Retina Tomogram Layer Boundary Detection
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Solution Overview
Problem
Conventional image processing methods for ophthalmic tomograms struggle to accurately specify retina layer boundaries, particularly in the presence of artifacts such as blood vessels, hemorrhages, and lesions, which can alter the layer structure and make boundary detection challenging.
Innovation Solution
An image processing apparatus and method that detects and maps layer structures from tomograms, using a detection unit to identify structures, an appending unit to append structure information, and a mapping unit to generate a two-dimensional image, allowing for the specification of artifacts and lesions within the retina layer structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single algorithm is used to specify retina layer boundaries, then the processing is simple and fast, but the accuracy decreases when artifacts or lesions are present
Solution Approach 1:
The patent divides the retina layer boundary specification into multiple processing stages: initial boundary detection, artifact/lesion detection, area classification, and selective re-processing. This segmentation allows different algorithms to be applied to different regions based on their characteristics, improving overall accuracy without requiring all algorithms to run on all data.
Solution Approach 2:
The patent implements a dynamic processing approach where the algorithm automatically switches between different processing modes based on the detected characteristics of each region. Normal regions use standard processing while artifact-affected regions use specialized processing, creating an adaptive system that optimizes accuracy for each local area.
2Measurement precision
If artifact areas are extensively processed to improve accuracy, then the measurement precision improves, but the processing time increases
Solution Approach 1:
The patent applies different processing qualities to different regions: full processing for normal regions and specialized artifact-processing only for detected artifact areas. This local quality approach ensures high accuracy where needed while minimizing processing time in normal regions, resolving the time-accuracy tradeoff.
Solution Approach 2:
The patent performs preliminary artifact detection and classification before applying intensive processing. By identifying artifact areas in advance and classifying them by type, the system prepares targeted processing strategies only for regions that need them, avoiding unnecessary processing time in normal areas.
3Adaptability or versatility
If conventional algorithms are used without differentiation, then the processing is uniform and simple, but the ability to handle different artifact types decreases
Solution Approach 1:
The patent creates a dynamic processing system that automatically adapts to different artifact types by detecting their characteristics and selecting appropriate processing strategies. The system transitions from static uniform processing to dynamic adaptive processing based on real-time artifact identification.
Solution Approach 2:
The patent changes processing parameters based on artifact type detection. Different artifact types (blood vessels, hemorrhages, lesions) trigger different processing parameters and algorithms, allowing the system to adapt to various conditions while maintaining a unified overall framework.
Data Source
AI summary
An image processing apparatus processes a plurality of tomograms obtained by acquiring, along a direction crossing at a right angle a section along the thickness direction of a retina, a plurality of tomograms each including the section. The apparatus detects a layer structure in the retina from image information of respective lines of the tomograms along the thickness direction, and appends structure information to the respective lines based on the layer structures detected for the respective lines. The image processing apparatus maps the structure information of the respective lines of the plurality of tomograms onto a plane crossing the thickness direction at a right angle, thereby generating a two-dimensional image based on the structure information.