3D Medical Image Segmentation via Adaptive Weighted Graph Cuts
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Solution Overview
Problem
Automatically segmenting regions of body organs in three-dimensional medical images, such as lung lobes and bronchioles, is challenging due to the difficulty in distinguishing between closely spaced structures with small gaps or variations in signal intensity.
Innovation Solution
A method and apparatus for segmenting medical images by setting seed groups of voxels, assigning weights to links based on node types and signal intensity differences, and repeatedly determining the minimum-weight path to segment the image into two regions, using a combination of preprocessing, seed group selection, and weight determination techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional automatic segmentation methods are used to divide body organ regions, then the process can be automated, but the segmentation accuracy deteriorates for closely spaced structures with small gaps or low signal intensity variations
Solution Approach 1:
The patent divides the segmentation process into multiple stages: initial seed point selection, iterative region growing with adaptive thresholds, and boundary refinement. This multi-stage segmentation approach maintains automation while improving accuracy by handling different aspects of the segmentation task separately with specialized algorithms for each stage.
Solution Approach 2:
The patent applies adaptive thresholding and local intensity analysis that adjusts segmentation parameters based on local image characteristics. Different regions with varying signal intensity and texture properties receive locally optimized segmentation criteria, enabling accurate separation of closely spaced structures with subtle intensity differences while maintaining automated operation.
2Productivity
If simple segmentation algorithms are used to maintain computational efficiency, then processing speed is improved, but the ability to handle non-uniform distances and signal intensity variations deteriorates
Solution Approach 1:
The patent performs preprocessing steps including intensity normalization, noise filtering, and initial seed point selection before the main segmentation process. This preliminary preparation optimizes the input data for subsequent processing, enabling faster convergence of the segmentation algorithm while improving its ability to handle non-uniform image characteristics through pre-computed statistical measures.
Solution Approach 2:
The patent employs adaptive thresholding and dynamically adjusted segmentation parameters that evolve during the processing based on local image statistics and convergence criteria. This dynamic adaptation allows the algorithm to efficiently handle varying distances and intensity patterns without requiring exhaustive search, maintaining computational speed while improving reliability across diverse image characteristics.
Data Source
AI summary
Provided is a method and apparatus for segmenting medical images. The apparatus sets a first seed group including voxels belonging to a segmentation target region among voxels of a medical image and a second seed group including voxels belonging to a remaining region thereof, assigns a weight to a link between a start node and an end node and a voxel node, and segments the medical image into two regions by cutting a link having a minimum weight in a shortest path in which a sum of weights of a path connecting the start node and the end node is minimum.