A bone segmentation method and device for ankle joint CT images

Through multiple threshold segmentation and connectivity domain analysis combined with image addition, the efficiency and accuracy of bone structure segmentation of ankle joint CT images are solved, and efficient and low-cost automatic segmentation of bone structure is achieved, which is suitable for bone structure segmentation of ankle joint CT images.

CN119180839BActive Publication Date: 2025-04-18GUANGZHOU PLANCK INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411107782.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-13
Publication Date
2025-04-18
Estimated Expiration
2044-08-13

AI Technical Summary

Technical Problem

The prior art has problems of incomplete segmentation and adhesion in bone segmentation of ankle joint CT images, and deep learning methods require a large amount of labeled data and high computing resources, making it difficult to achieve automatic segmentation of each bone structure efficiently and at low cost.

Method used

Multiple threshold segmentation, connectivity domain analysis and image addition operations are adopted, combined with the region growth algorithm, seed images are automatically generated, interfering structures are removed through custom threshold segmentation and connectivity domain analysis technology, seed images containing bone structures are generated, and a three-dimensional model is obtained through region growth and resampling technology.

Benefits of technology

It realizes efficient and accurate bone structure segmentation of ankle joint CT images, reduces labor costs and hardware requirements, improves segmentation speed and result clarity, avoids wrong growth and adhesion, and is suitable for automatic segmentation of multiple bone structures.

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Abstract

This invention discloses a method and apparatus for bone segmentation in ankle CT images. The invention acquires the ankle CT image to be segmented and the number of feet in the image. Based on the number of feet, it sequentially uses techniques such as threshold segmentation, connected component analysis, and image addition to generate a reference skeletal structure image and a seed image containing each skeletal structure and its corresponding label value. Using the reference skeletal structure image and the seed image, it acquires noisy label images with the same parameters as the ankle CT image to be segmented. These noisy label images are then denoised and filled to obtain label images containing the corresponding label values ​​for different skeletal structures and a 3D model file for each skeletal structure. This invention not only reduces the manpower and computational requirements of the bone segmentation process but also improves accuracy and efficiency.
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