We present a novel way to perform image registration , which is not limited to a specific kind, between image pairs with very large deformation , while preserving Quasiconformal property without tedious manual landmark labeling that conventional mathematical registration methods require. Alongside the practical function of our algorithm, one just-as-important underlying message is that the integration between typical CNN and existing Mathematical model is successful as will be pointed out by our paper, meaning that machine learning and mathematical model could coexist, cover for each other and significantly improve registration result. This paper will demonstrate an unprecedented idea of making use of both robustness of CNNs and rigorousness of mathematical model to obtain meaningful registration maps between 2D images under the aforementioned strict constraints for the sake of well-posedness .
Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling and are prone to error. In recent years, there has been a surge of interest in the use of machine learning for image registration. However, most learning-based methods cannot ensure the bijectivity of the registration, which makes it difficult to establish a 1-1 correspondence between the images. In this paper, we develop a novel method for large deformation image registration by a fusion of convolutional neural network (CNN) and quasiconformal theory. More specifically, we propose a new fidelity term for incorporating the CNN features in our quasiconformal energy minimization model, which enables us to obtain meaningful registration results without prescribing any landmarks. Moreover, unlike other learning-based methods, the bijectivity of our method is guaranteed by quasiconformal theory. Experimental results are presented to demonstrate the effectiveness of the proposed method. More broadly, our work sheds light on how rigorous mathematical theories and practical machine learning approaches can be integrated for developing computational methods with improved performance .
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We present a novel way to perform image registration , which is not limited to a specific kind, between image pairs with very large deformation , while preserving Quasiconformal property without tedious manual landmark labeling that conventional mathematical registration methods require. Alongside the practical function of our algorithm, one just-as-important underlying message is that the integration between typical CNN and existing Mathematical model is successful as will be pointed out by our paper, meaning that machine learning and mathematical model could coexist, cover for each other and significantly improve registration result. This paper will demonstrate an unprecedented idea of making use of both robustness of CNNs and rigorousness of mathematical model
Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling and are prone to error. In recent years, there has been a surge of interest in the use of machine learning for image registration. However, most learning-based methods cannot ensure the bijectivity of the registration, which makes it difficult to establish a 1-1 correspondence between the images. In this paper, we develop a novel method for large deformation image registration by a fusion of convolutional neural network (CNN) and quasiconformal theory. More specifically, we propose a new fidelity term for incorporating the CNN features in our quasiconformal energy minimization model, which enables us
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maps between 2D images under the aforementioned strict constraints for the sake of well-posedness
results without prescribing any landmarks. Moreover, unlike other learning-based methods, the bijectivity of our method is guaranteed by quasiconformal theory. Experimental results are presented to demonstrate the effectiveness of the proposed method. More broadly, our work sheds light on how rigorous mathematical theories and practical machine learning approaches can be integrated for developing computational methods with improved performance