Intelligent identification system for osteoporosis area

By combining traditional image processing and deep learning technology, an intelligent identification system for osteoporosis areas is realized, supporting multimodal medical image input, and generating personalized reports, solving the problem of difficult to balance detection efficiency and accuracy of existing systems, and meeting the hospital's real-time diagnosis needs.

CN120495241AInactive Publication Date: 2025-08-15XUZHOU YACHUANG BIOLOGICAL TECH CO LTD
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Patent Information

Application Number
CN202510590658.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-08
Publication Date
2025-08-15
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The existing intelligent identification system for osteoporosis areas is difficult to balance the detection efficiency and accuracy, lacks a dynamic optimization mechanism, cannot generate detailed bone density reports that meet international standards, cannot provide personalized risk warnings, and cannot operate efficiently on medical equipment, making it difficult to meet the hospital's real-time diagnosis needs.

Method used

The image acquisition and preprocessing module, candidate area positioning and optimization module, multimodal feature extraction module, feature fusion and weighting module, classification decision-making and clinical reporting module, data enhancement and model optimization module, and real-time reasoning and feedback module are adopted, combined with traditional image processing and deep learning technology, bone trabecular texture features are extracted through Gabor filters and local binary mode algorithms, and personalized reports are generated using cascade classifiers, and real-time diagnosis is achieved through embedded deployment and dynamic parameter optimization.

Benefits of technology

It significantly improves the detection capabilities of osteoporosis areas, supports multimodal medical imaging input, realizes real-time and efficient osteoporosis diagnosis, generates detailed reports that meet international standards, provides personalized risk warnings, reduces the false detection rate, and meets the hospital's real-time diagnosis needs.

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Abstract

The invention discloses an intelligent identification system for an osteoporosis area, belongs to the field of image processing calculation, and aims to solve the problems of limited technical coverage and lack of a dynamic optimization mechanism. In a feature extraction stage, a system combines traditional image processing and deep learning technologies in parallel, extracts bone trabecula multidirectional texture features by using a Gabor filter and a local binary pattern algorithm, analyzes bone contour curvature by combining Sobel edge detection and morphological operation, constructs geometric morphological parameters, and performs feature extraction on the bone trabecula. The local branch focuses on the porosity and arrangement rule of the bone trabecula by adopting a 3D convolutional network, the global branch is embedded into a compression excitation module based on an improved MobileNetV3 network to strengthen the overall morphological expression of the bone, the response intensity of a local microstructure is enhanced by space attention, the weight of global bone topological characteristics is calibrated by channel attention, and a multi-scale splicing strategy is combined, so that the overall morphological expression of the bone trabecula is optimized. And finally, outputting a fusion feature matrix after noise suppression, and remarkably improving the expression ability of pathological features.
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