基于多模态牙科图像的牙体硬组织自动配准

牙科图像由于成像模式、图像质量、不同病人之间存在差异性，导致临床应用中牙体硬组织的精确配准成为难点。针对这些问题，并根据相关研究工作，提出了一种更适用于多模态牙科图像的牙体硬组织自动配准方法。该方法基于改进的ICP算法，对多模态的牙科荧光图像和自然光图像进行分析。首先，根据多模态牙科图像的特点，算法对图像进行了预处理；其次，研究了鲁棒的特征点提取方法，即将牙体硬组织边缘选取为特征点，并同时根据口腔病理学先验知识，提取多模态图像中的病损组织区域，进一步优化了配准点集；最后，利用改进的ICP算法对齿科图像进行了配准，配准过程中对ICP的迭代策略和鲁棒损失函数进行了分析和优化。实验结果表明，该方法能更快速地收敛，且具有更好的鲁棒性和准确性。

Dental hard tissue registration based on multi-modality dental images

Dental hard tissue registration is very challenging due to the complexity of histological structure, the variability of imaging modality and image quality. To benefit from the advantages of the relative works, this paper proposed a novel dental hard tissue auto-registration method based on multi-modality dental images. It employed the modified ICP algorithm and was especially effective for dental fluorescence imaging and the reflectance imaging. The algorithm firstly pre-processed the dental images to decrease the impact of imbalance local illumination. Secondly, it investigated a robust feature points extraction strategy, i. e. calculated the boundaries of hard tissue as initial feature point sets, and further extracted the lesion areas based on the prior knowledge of oral histopathology for the refinement of feature points. Finally, this paper executed the modified ICP algorithm on the basis of the ICP iterative method and robust loss function to register dental image. The experimental results show that the proposed method is not only robust, but also can converge more quickly and achieve higher accuracy.