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An evaluation of image registration methods for chest radiographs

机译:胸部X光片图像配准方法的评价

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Image registration is commonly used in medical applications for revealing changes in different series of images. In this study, the performances of different registration scenarios based on different feature extraction and matching methods were assessed in the context of chest radiographic images. For this purpose, combination of three well known key point descriptors (SIFT, SURF and ORB) were used as feature detectors. For feature matching, SIFT and SURF methods were also employed individually. The tests were conducted on chest X-ray images of real patient data taken at different times. The accuracies of the registered images were assessed by two different validation algorithms. The experiments revealed that the highest registration accuracy is achieved when SIFT and SURF descriptors are used together for key point extraction, and SIFT algorithm is used for feature matching.
机译:图像配准通常用于医疗应用中,以揭示不同系列图像中的变化。在这项研究中,在胸部放射线图像的背景下评估了基于不同特征提取和匹配方法的不同配准方案的性能。为此,将三个众所周知的关键点描述符(SIFT,SURF和ORB)组合用作特征检测器。对于特征匹配,还分别采用了SIFT和SURF方法。测试是在不同时间获取的真实患者数据的胸部X射线图像上进行的。配准图像的准确性通过两种不同的验证算法进行评估。实验表明,将SIFT和SURF描述符一起用于关键点提取,而SIFT算法用于特征匹配,则可以实现最高的配准精度。

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