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Proceedings Paper

Microscopic neural image registration based on the structure of mitochondria
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Paper Abstract

Microscopic image registration is a key component of the neural structure reconstruction with serial sections of neural tissue. The goal of microscopic neural image registration is to recover the 3D continuity and geometrical properties of specimen. During image registration, various distortions need to be corrected, including image rotation, translation, tissue deformation et.al, which come from the procedure of sample cutting, staining and imaging. Furthermore, there is only certain similarity between adjacent sections, and the degree of similarity depends on local structure of the tissue and the thickness of the sections. These factors make the microscopic neural image registration a challenging problem. To tackle the difficulty of corresponding landmarks extraction, we introduce a novel image registration method for Scanning Electron Microscopy (SEM) images of serial neural tissue sections based on the structure of mitochondria. The ellipsoidal shape of mitochondria ensures that the same mitochondria has similar shape between adjacent sections, and its characteristic of broad distribution in the neural tissue guarantees that landmarks based on the mitochondria distributed widely in the image. The proposed image registration method contains three parts: landmarks extraction between adjacent sections, corresponding landmarks matching and image deformation based on the correspondences. We demonstrate the performance of our method with SEM images of drosophila brain.

Paper Details

Date Published: 24 February 2017
PDF: 6 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013325 (24 February 2017); doi: 10.1117/12.2254208
Show Author Affiliations
Huiwen Cao, Institute of Automation (China)
Hua Han, Institute of Automation (China)
Ctr. for Excellence in Brain Science and Intelligence Technology (China)
Qiang Rao, Institute of Automation (China)
Chi Xiao, Institute of Automation (China)
Xi Chen, Institute of Automation (China)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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