Abstract
There are many kinds of orthopedic diseases with complex professional background, and it is easy to miss diagnosis and misdiagnosis. The computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes. It is of great significance to assist orthopedic doctors to diagnose orthopedic diseases from the perspective of visual vision and quantitative indicators, which can improve the diagnosis rate and accuracy of orthopedic diseases, reduce the pain of patients, and shorten the treatment time of diseases. To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and Powell. Through the linear search strategy of golden section method and Powell algorithm optimization, the best spatial transformation parameters are found, which maximizes the normalized mutual information between images to be registered, thus ensuring the consistency of two-dimensional spatial positions. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. The system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. The experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. The system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions.
Highlights
Due to the improvement of living standards, the number of patients with orthopedic diseases is rising day by day. e number of patients with lumbar spondylosis in China alone reaches 15.2% of the total population, which has become another major disease endangering human health. ere are many kinds of orthopedic diseases
Computer-aided diagnosis technology is called the “third eye” of doctors, which can greatly improve the diagnosis speed of doctors, and improve the accuracy of diagnosis results. erefore, it is of great practical significance to use computer-aided diagnosis technology to diagnose orthopedic diseases [1]
Combined with medical image processing technologies such as image segmentation, registration, and visualization, useful information can be visually presented by integrating index values, two-dimensional images, and three-dimensional models
Summary
Received 9 September 2021; Revised 30 October 2021; Accepted 1 November 2021; Published 22 November 2021. E computer-aided diagnosis system of orthopedic diseases based on the key technology of medical image processing can locate and display the lesion location area by visualization, measuring and providing disease diagnosis indexes. To solve the problem of possible spatial inconsistency of medical images of orthopedic diseases, we propose an image registration method based on volume feature point selection and Powell. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. E system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. E experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. According to the proposed algorithm, a computer-aided diagnosis system of orthopedic diseases is developed and designed independently. e system consists of five modules, which can complete many functions such as medical image input and output, algorithm processing, and effect display. e experimental results show that the system developed in this paper has good results in cartilage tissue segmentation, bone and urate agglomeration segmentation, urate agglomeration artifact removal, two-dimensional and three-dimensional image registration, and visualization. e system can be applied to clinical gout and cartilage defect diagnosis and evaluation, providing sufficient basis to assist doctors in making diagnosis decisions
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