Abstract
In order to deal with the problems of poor anti-interference, low reconstruction clarity and large errors in the traditional medical image reconstruction methods, we proposed a fuzzy medical images three-dimensional (3D) reconstruction method using quantum algorithm. First of all, a feature matching model of fuzzy medical image was built. Secondly, this study decomposed the edge contour features by using the Gaussian mixture feature matching method, extracted the edge contour vectors of fuzzy medical image, and enhanced the information of fuzzy medical images by adopting the region edge sharpening. Thirdly, this study reorganized the 3D texture structure of images and reconstructed the sparse scattered points according to its texture and detail regions. Finally, we combined with the gray histogram of fuzzy medical images to achieve the adaptive pixel reconstruction of fuzzy medical images, and completed the 3D reconstruction of fuzzy medical images by employing the quantum algorithm. The results show that the proposed method is characterized by high matching degree of image features and balanced distribution of point clouds, and the self-similarity coefficient of the reconstructed texture can reach 0.994; in addition, the SINR value of the reconstruction result can be maintained around 100dB, and it has lower error rate than the traditional method, thereby improving the detection and recognition capability of medical images, and the algorithm has certain practical application.
Highlights
Medical images have the characteristic of special shape
Based on the existing research results of related quantum computing, this study introduced the quantum algorithm for fuzzy medical image 3D reconstruction, constructed a feature matching model, decomposed the edge contour features of images, and performs information enhancement; on this basis, reconstructed the sparse scattered points, and completed the 3D reconstruction of the fuzzy medical images based on the quantum algorithm
Based on the Gaussian mixture feature matching method for edge contour feature decomposition of fuzzy medical images, this study carries out the fuzzy medical image 3D reconstruction
Summary
Medical images have the characteristic of special shape. there are usually phenomena such as low image contrast, frequent changes in tissue characteristics, and fuzzy regional and boundary features. Based on the existing research results of related quantum computing, this study introduced the quantum algorithm for fuzzy medical image 3D reconstruction, constructed a feature matching model, decomposed the edge contour features of images, and performs information enhancement; on this basis, reconstructed the sparse scattered points, and completed the 3D reconstruction of the fuzzy medical images based on the quantum algorithm. In order to realize the 3D reconstruction of fuzzy medical images, it is necessary to linearly superimpose the composite variable of fuzzy medical image in the quantum space using the extraction of edge contour feature quantity and information enhancement processing, and complete the image 3D reconstruction by using the quantum space operation.
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