Dongsheng Jiang1, Yonghong Shi1, Xinrong Chen1, Manning Wang1, Zhijian Song1. 1. 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.
Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.