Matching MRI brain images between patients or mapping patients' MRI slices to the simulated atlas of a brain is key to the automatic registration of MRI of a brain. The ability to match MRI images would also enable such applications as indexing and searching MRI images among multiple patients or selecting images from the region of interest. In this work, we have introduced robustness, accuracy and cumulative distance metrics and methodology that allows us to compare different techniques and approaches in matching brain MRI of different patients or matching MRI brain slice to a position in the brain atlas. To that end, we have used feature detection methods AGAST, AKAZE, BRISK, GFTT, HardNet, and ORB, which are established methods in image processing, and compared them on their resistance to image degradation and their ability to match the same brain MRI slice of different patients. We have demonstrated that some of these techniques can correctly match most of the brain MRI slices of different patients. When matching is performed with the atlas of the human brain, their performance is significantly lower. The best performing feature detection method was a combination of SIFT detector and HardNet descriptor that achieved 93% accuracy in matching images with other patients and only 52% accurately matched images when compared to atlas.

翻译：患者间脑部MRI图像的匹配，或患者MRI切片与模拟脑图谱的对应，是脑部MRI自动配准的关键。MRI图像匹配能力还可支持多患者MRI图像的索引与检索、感兴趣区域图像选择等应用。本研究引入了鲁棒性、精度、累积距离等度量指标及方法论，使得我们能够比较不同技术在匹配不同患者脑部MRI或匹配脑MRI切片至脑图谱位置时的表现。为此，我们采用了图像处理领域成熟的特征检测方法——AGAST、AKAZE、BRISK、GFTT、HardNet和ORB，并比较了它们对图像退化的抵抗能力以及匹配不同患者同一脑部MRI切片的能力。研究证明，其中部分技术能正确匹配大多数不同患者的脑部MRI切片。但当与人类脑图谱进行匹配时，其性能显著下降。表现最佳的特征检测方法为SIFT检测器与HardNet描述子的组合，其在患者间图像匹配中达到93%的准确率，而与图谱匹配时准确率仅为52%。