Adaptive Tomogram Analysis for Eye Region Diagnosis
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Solution Overview
Problem
Existing techniques for eye region diagnosis, such as OCT, require multiple processing methods and parameters for detecting various diseases, leading to increased user load and inefficient calculation processes.
Innovation Solution
An image processing apparatus and method that adaptively acquires eye region diagnosis information by detecting predetermined layers, changing algorithms based on extracted information, and quantifying lesions for multiple disease diagnoses from tomograms without increasing user load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple processing methods and parameters are used for detecting various eye diseases, then the comprehensiveness of diagnosis is improved, but the user load and calculation complexity increase
Solution Approach 1:
The patent implements a single image processing apparatus that performs multiple diagnostic functions by automatically selecting and executing different processing methods based on the input tomogram characteristics. The system can detect various eye diseases (glaucoma, age-related macular degeneration, diabetic retinopathy, etc.) using one unified platform that adapts its processing approach according to the detected layer states and disease patterns.
Solution Approach 2:
The system dynamically adjusts its processing methodology based on real-time analysis of the tomogram data. It automatically determines the appropriate processing method by evaluating layer boundary detection results and layer state information, then switches between different diagnostic algorithms without requiring manual user intervention or parameter adjustment.
2Adaptability or versatility
If multiple processing methods are executed for different diseases, then diagnostic coverage is improved, but calculation time and processing efficiency deteriorate
Solution Approach 1:
The patent segments the diagnostic process into distinct processing methods, each optimized for specific disease types. The system divides the overall diagnosis into layer detection, layer state determination, and disease-specific analysis stages, allowing it to apply only the necessary processing steps for each case rather than executing all possible diagnostic algorithms.
Solution Approach 2:
The system performs preliminary layer detection and state determination before executing disease-specific processing. By pre-analyzing the tomogram to identify which layers are present and their states, the system can select and apply only the relevant processing methods needed for the detected conditions, avoiding unnecessary calculations.
3Ease of operation
If fixed analysis methods are used, then processing simplicity is maintained, but the ability to detect multiple disease types is limited
Solution Approach 1:
The image processing apparatus performs self-service by automatically determining which processing methods to apply based on its analysis of the input tomogram. The system autonomously evaluates layer detection results and layer states, then selects appropriate diagnostic algorithms without requiring user input or manual configuration, maintaining operational simplicity while achieving versatile disease detection.
Solution Approach 2:
The system uses feedback from layer detection and state determination results to dynamically select processing methods. The output of preliminary analysis feeds into the decision-making process for choosing subsequent diagnostic steps, creating an adaptive loop that maintains simplicity while enhancing disease detection capability based on actual data characteristics.
Data Source
AI summary
There is provided a technique for adaptively acquiring, from a tomogram of an eye region, diagnosis information data of the eye region which is used for the diagnosis of a plurality of kinds of diseases, without increasing load on a user. A layer acquisition unit (331) acquires a predetermined layer area from the tomogram of the eye region. A changing unit (332) changes an algorithm for the acquisition of diagnosis information data as information used for the diagnosis of the eye region from the tomogram based on the information data extracted from the layer area. A quantifying unit (336) acquires diagnosis information data from the tomogram based on the changed algorithm.