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Issues involved in automatic selection and intensity based matching of feature points for mls registration of medical images

机译:医学图像的mls配准的特征点自动选择和基于强度的匹配中涉及的问题

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Most of the Registration algorithms require selection of corresponding control points from the source and the target images. The MLS being a point based method requires the selection of control points for registration. The accuracy of registration process depends greatly on the selected control points. Hence, feature detection and matching play an important role in the process of point based registration of medical images. Therefore an analysis on the consequence of automating the control point selection process using feature extraction algorithms like Harris Corner, Min-Eigen, Speeded Up Robust Features (SURF) and Canny Edge pixels is deemed to be essential. Since the end users are medical practitioners, who prefer to have an interactive system, where the control points need to be selected based on the diagnostic requirements analysis on manual selection of control points has also been included. The issues involved in automatic control point selection from MRI/CT images have been discussed in this work.
机译:大多数配准算法都需要从源图像和目标图像中选择相应的控制点。作为基于点的方法,MLS需要选择用于注册的控制点。注册过程的准确性很大程度上取决于所选的控制点。因此,特征检测和匹配在基于点的医学图像配准过程中起着重要的作用。因此,使用Harris Corner,Min-Eigen,加速鲁棒特征(SURF)和Canny Edge像素等特征提取算法对控制点选择过程进行自动化的结果的分析被认为是必不可少的。由于最终用户是医疗从业者,他们更喜欢使用交互式系统,因此还需要根据诊断需求分析对控制点进行手动选择来选择控制点。在这项工作中已经讨论了从MRI / CT图像中选择自动控制点所涉及的问题。

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