Application of Dual Modality Contrast Agent Combined with Multi-Scale Representation in Ultrasound-Magnetic Resonance Imaging Registration Scheme |
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Authors: | Mo Hou Weiyu Kevin Chiang Weiqiang Hong Maoyun Yang Wenhua Yu |
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Institution: | 1 School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China2 College of Business, City University of Hong Kong, Hong Kong, 999077, China3 School of Mathematics Science, Jiangsu Normal University, Xuzhou, 221116, China4 Key Laboratory of Education Big Data Science and Engineering, Jiangsu Normal University, Xuzhou, 221116, China |
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Abstract: | To achieve the image registration/fusion and perfect the quality of the
integration, with dual modality contrast agent (DMCA), a novel multi-scale representation registration method between ultrasound imaging (US) and magnetic
resonance imaging (MRI) is presented in the paper, and how DMCA influence
on registration accuracy is chiefly discussed. Owing to US’s intense speckle noise,
it is a tremendous challenge to register US with any other modality images. How
to improve the algorithms for US processing has become the bottleneck, and in
the short term it is difficult to have a breakthrough. In that case, DMCA is
employed in both US and MRI to enhance the region of interest. Then, because
multi-scale representation is a strategy that attempts to diminish or eliminate several possible local minima and lead to convex optimization problems to be solved
quickly and more efficiently, a multi-scale representation Gaussian pyramid based
affine registration (MRGP-AR) scheme is constructed to complete the US-MRI
registration process. In view of the above-mentioned method, the comparison tests
indicate that US-MRI registration/fusion may be a remarkable method for gaining
high-quality registration image. The experiments also show that it is feasible that
novel nano-materials combined with excellent algorithm are used to solve some
hard tasks in medical image processing field. |
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Keywords: | US MRI accuracy registration DMCA MRGP-AR |
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