Jiang Dongsheng, Shi Yonghong, Chen Xinrong, Wang Manning, Song Zhijian
Digital Medical Research Center of School of Basic Medical Sciences, Fudan University and Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, 138 YiXue Yuan Road, Shanghai, 200032, China.
Med Phys. 2017 Feb;44(2):497-509. doi: 10.1002/mp.12049.
Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations.
dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications.
dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference.
The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention.
可变形多模态图像配准通过提供互补信息有助于放射治疗和图像引导手术，但由于难以定义合适的相似性度量，在医学图像分析领域仍然是一项具有挑战性的任务。本文提出了一种新颖、稳健且快速的二进制描述符，即判别局部导数模式（dLDP），它能够将不同模态的图像编码为相似的图像表示。
dLDP根据其邻域内强度导数的模式为每个体素计算一个二进制字符串。使用汉明距离评估描述符相似性，汉明距离可以高效计算，而不是传统的L1或L2范数。我们首次通过多个多模态配准应用验证了局部导数模式在多模态可变形图像配准中的有效性和可行性。
在人工图像和临床环境中，将dLDP与三种最先进的方法进行了比较。在来自BrainWeb的不同磁共振成像（MRI）模态之间、患者数据的计算机断层扫描和MRI图像之间以及来自BITE数据库的MRI和超声图像之间的可变形配准实验中，我们表明我们的方法在准确性和时间效率方面均优于局部互信息和熵图像。我们进一步验证了dLDP在术前MRI和三维术中超声图像的可变形配准中的有效性。我们的结果表明，dLDP将平均平均目标配准误差从4.12毫米降低到2.30毫米。该准确性在统计学上与该研究中最先进方法的准确性相当；然而，在计算复杂度方面，我们的方法明显优于其他方法，甚至与绝对差之和相当。
结果表明，dLDP在一般多模态图像配准中在准确性和时间效率方面均能实现卓越性能。此外，dLDP还显示了临床超声引导干预的潜力。
