6 April 2016 Brain vascular image enhancement based on gradient adjust with split Bregman
Author Affiliations +
Light Sheet Microscopy is a high-resolution fluorescence microscopic technique which enables to observe the mouse brain vascular network clearly with immunostaining. However, micro-vessels are stained with few fluorescence antibodies and their signals are much weaker than large vessels, which make micro-vessels unclear in LSM images. In this work, we developed a vascular image enhancement method to enhance micro-vessel details which should be useful for vessel statistics analysis. Since gradient describes the edge information of the vessel, the main idea of our method is to increase the gradient values of the enhanced image to improve the micro-vessels contrast. Our method contained two steps: 1) calculate the gradient image of LSM image, and then amplify high gradient values of the original image to enhance the vessel edge and suppress low gradient values to remove noises. Then we formulated a new L1-norm regularization optimization problem to find an image with the expected gradient while keeping the main structure information of the original image. 2) The split Bregman iteration method was used to deal with the L1-norm regularization problem and generate the final enhanced image. The main advantage of the split Bregman method is that it has both fast convergence and low memory cost. In order to verify the effectiveness of our method, we applied our method to a series of mouse brain vascular images acquired from a commercial LSM system in our lab. The experimental results showed that our method could greatly enhance micro-vessel edges which were unclear in the original images.
Conference Presentation
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Xiao Liang, Xiao Liang, Di Dong, Di Dong, Hui Hui, Hui Hui, Liwen Zhang, Liwen Zhang, Mengjie Fang, Mengjie Fang, Jie Tian, Jie Tian, } "Brain vascular image enhancement based on gradient adjust with split Bregman", Proc. SPIE 9711, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IX, 97111D (6 April 2016); doi: 10.1117/12.2211627; https://doi.org/10.1117/12.2211627

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