Abstract
Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.
Highlights
Medical image registration technology is a widely used image processing technology in the field of medicine image analysis [1]
When there is moderate noise in the image and there is translation and scaling between the multimodality images, phase correlation image registration technology is an effective method for subpixel image registration. is paper proposes an improved algorithm based on Guizar-Sicairos registration, which can quickly search for the offset between registered images and greatly reduce the time and space complexity of registration without losing the registration accuracy
We improve the performance of single-step discrete Fourier transform by reducing the dimension of Fourier transform cross-correlation matrix and the number of DFT matrix multiplication used to locate the peak value
Summary
Medical image registration technology is a widely used image processing technology in the field of medicine image analysis [1]. It plays an important role in human 3D modeling, multisource medical image fusion, the lesion feature detection and extraction, and other auxiliary diagnoses [2]. E internal structure and function of the human body can be reflected through the image, providing intuitive human anatomy, physiology, and pathology information. At this time, the image configuration technology needs to solve the problem of position registration of fusion between images. When there is moderate noise in the image and there is translation and scaling between the multimodality images, phase correlation image registration technology is an effective method for subpixel image registration. is paper proposes an improved algorithm based on Guizar-Sicairos registration, which can quickly search for the offset between registered images and greatly reduce the time and space complexity of registration without losing the registration accuracy
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