Blood Vessel Image Extraction Using Scalp Removal and MIP
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Solution Overview
Problem
Existing blood vessel imaging technologies struggle to accurately and efficiently extract blood vessels from original images that include various types of organs and tissues, hindering precise diagnosis of vascular diseases.
Innovation Solution
A method and system that utilize a computing device to obtain an original image, remove peripheral regions such as the scalp, and generate target images through maximum intensity projection operations, aided by segmentation models and recognition models to enhance accuracy and efficiency in blood vessel extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual extraction methods are used to remove scalp and other tissues from original images, then the extraction accuracy may be maintained, but the user workload and time consumption increase significantly
Solution Approach 1:
The system enables automatic self-service extraction of blood vessels from original images using deep learning models. The segmentation model automatically identifies and removes scalp and other tissues without requiring manual user intervention, thus reducing user workload while maintaining high extraction accuracy through automated intelligent processing.
Solution Approach 2:
The patent replaces manual mechanical extraction operations with automated computational processing. Deep learning-based segmentation models substitute for manual image processing, using algorithms to automatically identify blood vessel regions and remove interfering tissues, thereby reducing user workload while preserving extraction precision.
2Measurement precision
If traditional image processing methods are used to extract blood vessels, then the process is simpler, but the extraction accuracy and efficiency are insufficient for precise diagnosis
Solution Approach 1:
The patent introduces an intermediate processing stage using segmentation models and recognition models as mediators between the original image and the final extracted blood vessel image. These intermediary deep learning models enhance extraction accuracy by intelligently identifying and removing interfering tissues, while the system manages processing complexity through modular model architecture and automated workflows.
Solution Approach 2:
The system changes the processing parameters by employing deep learning-based segmentation and recognition models instead of traditional image processing algorithms. This parameter change enables more accurate blood vessel extraction by learning complex patterns and features from training data, improving extraction accuracy while the modular implementation keeps processing complexity manageable.
3Measurement precision
If comprehensive tissue removal is performed to achieve clean blood vessel extraction, then the diagnostic precision improves, but the processing time increases
Solution Approach 1:
The system performs preliminary action by pre-training segmentation and recognition models on extensive datasets before actual image processing. This preliminary training enables the models to quickly and accurately identify blood vessels and remove interfering tissues during actual processing, thereby achieving high diagnostic precision without excessive processing time during clinical use.
Solution Approach 2:
The patent implements continuous useful action through automated end-to-end processing workflows. The segmentation model continuously processes images to remove interfering tissues, and the recognition model continuously identifies blood vessel structures, maintaining high diagnostic precision while minimizing processing time through uninterrupted automated operation without manual intervention.
Data Source
AI summary
The present disclosure is related to systems and methods for image processing. The method includes obtaining an original image. The original image includes at least one blood vessel region and at least one scalp region. The method includes determining an intermediate image by removing the at least one scalp region from the original image. The method includes generating at least one target image by performing a maximum intensity projection operation on the intermediate image. The at least one target image represents the at least one blood vessel region in the original image.