Fundus Image Processing Using Deep Learning Block Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for processing fundus images are inefficient, relying on complex image segmentation algorithms that require significant time and computational resources, making them cumbersome and resource-intensive.
Innovation Solution
A method involving the acquisition of a target fundus image, division into image blocks, and processing using a pre-trained deep learning model to determine the presence of specific image regions based on output values and thresholds, reducing computational load and improving processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image segmentation algorithms are used for processing fundus images, then measurement precision can be improved, but device complexity and computational resources increase significantly
Solution Approach 1:
The fundus image is divided into multiple image blocks, and a deep learning model is trained to identify predetermined regions in these blocks. This segmentation approach simplifies the overall processing by breaking down the complex image analysis into manageable blocks, reducing algorithmic complexity while maintaining identification accuracy.
Solution Approach 2:
The deep learning model is pre-trained offline using a large dataset of fundus images with predetermined regions. This preliminary training phase allows the model to learn complex patterns in advance, so that during actual processing, only inference is needed, significantly reducing computational complexity and processing time while maintaining high precision.
2Measurement precision
If complex image segmentation algorithms are used for processing fundus images, then measurement precision can be improved, but processing time increases significantly
Solution Approach 1:
The deep learning model undergoes extensive pre-training offline before deployment. This preliminary action transfers the computationally intensive learning process to the training phase, allowing the model to quickly perform inference during actual fundus image processing, thereby reducing processing time while maintaining high identification accuracy.
Solution Approach 2:
By dividing the fundus image into multiple blocks and using a pre-trained model to process each block independently, the system can parallelize processing and avoid the need for complex real-time segmentation algorithms, significantly reducing processing time while preserving measurement precision.
3Measurement precision
If complex image segmentation algorithms are used for processing fundus images, then measurement precision can be improved, but computational resources increase significantly
Solution Approach 1:
The computationally intensive training phase is performed preliminarily and offline, allowing the model to be deployed with minimal computational requirements for inference. This shifts the energy consumption to the initial training phase rather than continuous processing, reducing the computational resources needed during actual fundus image analysis while maintaining high precision.
Solution Approach 2:
Dividing the image into blocks enables more efficient processing by the pre-trained model, reducing the computational burden compared to processing the entire image with complex segmentation algorithms, thereby lowering energy consumption while preserving identification accuracy.
Data Source
AI summary
Embodiments of the present disclosure provide a method and apparatus for processing a fundus image. The method may include: acquiring a target fundus image; dividing the target fundus image into at least two first image blocks; inputting a first image block into a pre-trained deep learning model, to obtain a first output value; and determining, based on the first output value and a threshold, whether the first image block is the fundus image block containing a predetermined type of image region.