Serial-section electron microscopy is a widely used technique for neuronal circuit reconstruction. However, the continuity of neuronal structure is destroyed when the tissue block is cut into a series of sections. The neuronal morphology in different sections changes with their locations in the tissue block. These content changes in adjacent sections bring a difficulty to the registration of serial electron microscopy images. As a result, the accuracy of image registration is strongly influenced by neuronal structure variation and section thickness. To evaluate registration performance, we use the spherical deformation model as a simulation of the neuron structure to analyze how registration accuracy is affected by section thickness and neuronal structure size. We mathematically describe the trend that the correlation of neuronal structures in two adjacent sections decreases with section thickness. Furthermore, we demonstrate that registration accuracy is negatively correlated with neuronal structure size and section thickness by analyzing the second-order moment of estimated translation. The experimental results of registration on synthetic data demonstrate that registration accuracy decreases with the neuronal structure size.
Registration of electron microscopy (EM) images is one of the most important steps in reconstructing neurons. Image registration algorithm based on SIFT have been widely used in the EM image registration. But SIFT matching procedure both costs a lot of time and introduce massive false matches. In this paper, we propose an improved EM image registration method using the scale information of SIFT keypoints. In the feature matching procedure, our method saves up to 45.8% of the computation time compared to SIFT. We also added a preprocessing procedure for RANSAC to eliminate false matches in small-scale matches sets. Experimental results show that the method improves the accuracy of results on every test EM image set while highly reducing the registration time.
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