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A fast and accurate method to register medical images using Wavelet Modulus Maxima

机译:利用小波模量极大值快速准确地注册医学图像的方法

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摘要

This paper presents a fast, accurate and automatic method to register images of rigid bodies. It uses wavelets to obtain control points. Wavelets are not shift invariant but the structures determined by the wavelet high pass image, the Modulus Maxima Image, provide the information necessary for a fast-rough convergence. These structures represent shapes from which we segment the invariant shapes for the images being registered. For example, the MRI and CT images of the brain can be considered as rigid bodies that do not undergo a change in shape over reasonable periods of time. By using a convex hull, we make the procedure insensitive to the internal changes in the object. Hence, even with the growth of tumors, the procedure registers brain images very accurately. The method uses the correlation coefficient to measure the similarity between images and to determine how well the images are registered. The method has been extensively tested with various types of images and in all cases the registration accuracy is very high. The correlation coefficient used to validate the registrations has deficiencies that occasionally required a visual inspection to terminate the algorithm after a point of marginal improvement.
机译:本文提出了一种快速,准确和自动的配准刚体图像的方法。它使用小波获取控制点。小波不是平移不变的,而是由小波高通图像(模量极大图像)确定的结构提供了快速粗略收敛所必需的信息。这些结构代表形状,从中我们可以分割出要注册图像的不变形状。例如,大脑的MRI和CT图像可被视为在合理的时间内不会发生形状变化的刚体。通过使用凸包,我们使过程对对象的内部变化不敏感。因此,即使肿瘤生长,该程序也可以非常准确地记录大脑图像。该方法使用相关系数来测量图像之间的相似度,并确定图像的配准程度。该方法已针对各种类型的图像进行了广泛测试,并且在所有情况下配准精度都非常高。用于验证注册的相关系数存在一些缺陷,有时需要进行目视检查以在略微改善一点后终止算法。

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