Application of U-Net Based Multiparameter Magnetic Resonance Image Fusion in the Diagnosis of Prostate Cancer

Medical image fusion technology has been widely used in clinical practice by doctors to better understand lesion regions through the fusion of multiparametric medical images. This paper proposes an automated fusion method based on a U-Net. Through neural network learning, a weight distribution is ge...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.33756-33768
Main Authors: Huang, Xunan, Zhang, Bo, Zhang, Xiaoling, Tang, Min, Miao, Qiguang, Li, Tanping, Jia, Guang
Format: Article
Language:English
Subjects:
Computer vision
Image fusion
Image processing
Image segmentation
Laplace equations
Laplacian pyramid
Magnetic resonance imaging
Medical diagnostic imaging
Medical image
Medical imaging
Neural networks
Physicians
Prostate cancer
Target recognition
Transforms
U-Net network
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Summary:Medical image fusion technology has been widely used in clinical practice by doctors to better understand lesion regions through the fusion of multiparametric medical images. This paper proposes an automated fusion method based on a U-Net. Through neural network learning, a weight distribution is generated based on the relationship between the image feature information and the multifocus training target. The MRI image pair of prostate cancer (axial T2-weighted and ADC map) is fused using a strategy based on local similarity and Gaussian pyramid transformation. Experimental results show that the fusion method can enhance the appearance of prostate cancer in terms of both visual quality and objective evaluation.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3061078