Spine Localization in CT Images via Bone Distribution Analysis
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Solution Overview
Problem
Automatic localization of the spine in CT images is computationally intensive and memory demanding due to the large amount of acquired image data, making it inefficient and resource-heavy.
Innovation Solution
A method and system that selectively acquire and process slice images based on bone distribution parameters, such as center of gravity and standard deviation, to reduce unnecessary data and focus on relevant spine information, allowing for efficient localization without significant loss of image detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic localization of spine in CT images is performed using all acquired image data, then localization reliability is improved, but computational power and memory requirements increase significantly
Solution Approach 1:
The patent extracts only the necessary slice images containing spine information from the complete CT dataset. By analyzing bone distribution parameters (center of gravity, standard deviation) in each slice, the system identifies and extracts only those slices relevant to spine localization, excluding unnecessary slices from lower body regions. This extraction reduces the dataset size while maintaining localization reliability.
Solution Approach 2:
The patent segments the CT image data into individual slice images and processes them independently. By evaluating bone distribution parameters for each slice separately, the system can selectively include or exclude slices based on their content, rather than processing the entire dataset as a single unit. This segmentation enables efficient resource utilization while maintaining comprehensive spine localization capability.
2Measurement precision
If all slice images are processed for spine localization, then localization accuracy is improved, but memory consumption increases
Solution Approach 1:
The system extracts only the necessary slice images from the complete CT dataset by evaluating bone distribution parameters. Slices with bone distributions characteristic of spine regions (appropriate center of gravity and standard deviation) are extracted and stored, while slices from non-spine regions are discarded. This extraction maintains localization accuracy by preserving all relevant spine information while significantly reducing memory requirements.
Solution Approach 2:
The patent performs preliminary analysis of bone distribution parameters (center of gravity, standard deviation) for each slice image before full processing. This preliminary action identifies which slices contain spine information and should be retained, allowing the system to pre-filter the dataset and reduce memory consumption before subsequent processing steps.
3Productivity
If slice images are selected based on bone distribution parameters, then computational efficiency is improved, but data loss may occur
Solution Approach 1:
The patent uses parameter changes in bone distribution characteristics (center of gravity position, standard deviation values) as selection criteria for identifying spine-related slices. By establishing appropriate parameter thresholds and ranges, the system can reliably distinguish spine slices from non-spine slices, maintaining computational efficiency while minimizing information loss through carefully calibrated parameter selection.
Data Source
AI summary
A method and a corresponding apparatus and system localizes a spine in an image, in particular a computed tomography (CT) image, of a human or animal body, allowing for a reduced need for computational power and/or memory on the one hand and assuring a reliable localization of the spine on the other hand. The method includes a) acquiring a plurality of slice images of at least a part of a human or animal body, and b) automatically selecting slice images and/or parts of slice images from the acquired plurality of slice images by considering at least one parameter (μz, νz, σz, Λz) characterizing a distribution of bones in the acquired slice images, wherein the selected slice images and/or parts of the slice images includes image information about the spine.