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EVALUATION OF MONOGENIC PHASE APPORT IN ULTRASOUND-MRI IMAGE REGISTRATION

机译:超声MRI图像配准中单相阶段的评估

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Medical image registration is of great interest nowadays and is considered a reliable technique to improve diagnosis, therapy and analysis. The majority of pixel-based registration methods use the native intensity during the registration methods. However, in the last years newer methods based on local-phase were proposed. So far, there is no objective validation method for evaluating and comparing these two registration strategies. Hence, the objective of this paper is to comparatively analyze the registration using both approaches on synthetically images which enable us to have a ground truth. We deeply analyze the use of local phase images, obtained using the phase of the monogenic signal, in automatic image registration algorithms. In order to have a ground truth we simulate ultrasound (US) images starting from magnetic resonance (MR) slices. We use a realistic approach, constructing a map of randomly distributed ultrasound scatterers and the simulation tool Field II. Moreover, we compare the standard intensity-based approach with the local phase-based approach and study the influence of input images on the success of registration algorithms using both affine transformation and Free-Form Deformation with B-splines. We also show how the use of local phase images affects the successful use of certain simple similarity metrics like sum of squared differences (SSD), sum of absolute differences (SAD), correlation coefficient (CC) and also more complicated metrics like mutual information (MI) and residual complexity (RC).
机译:如今,医学图像配准备受关注,被认为是改善诊断,治疗和分析的可靠技术。大多数基于像素的配准方法在配准方法中使用原始强度。但是,近年来,提出了基于局部相位的更新方法。到目前为止,还没有客观的验证方法来评估和比较这两种注册策略。因此,本文的目的是在合成图像上使用两种方法对配准进行比较分析,从而使我们能够掌握基本事实。我们对在自动图像配准算法中使用单基因信号的相位获得的局部相位图像进行了深入分析。为了掌握地面真相,我们从磁共振(MR)切片开始模拟超声(US)图像。我们使用一种现实的方法,构建了随机分布的超声散射体图和仿真工具Field II。此外,我们将基于强度的标准方法与基于局部相位的方法进行了比较,并使用仿射变换和带有B样条的自由形式变形研究了输入图像对配准算法成功的影响。我们还展示了本地相位图像的使用如何影响某些简单相似性指标的成功使用,例如平方差和(SSD),绝对差和(SAD),相关系数(CC)以及更复杂的度量(例如互信息)( MI)和剩余复杂度(RC)。

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