Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.
Stripe artifacts, caused by high-absorption or high-scattering structures in the illumination light path, are a common drawback in both unidirectional and multidirectional light sheet fluorescence microscopy (LSFM), significantly deteriorating image quality. To circumvent this problem, we present an effective multidirectional stripe remover (MDSR) method based on nonsubsampled contourlet transform (NSCT), which can be used for both unidirectional and multidirectional LSFM. In MDSR, a fast Fourier transform (FFT) filter is designed in the NSCT domain to shrink the stripe components and eliminate the noise. Benefiting from the properties of being multiscale and multidirectional, MDSR succeeds in eliminating stripe artifacts in both unidirectional and multidirectional LSFM. To validate the method, MDSR has been tested on images from a custom-made unidirectional LSFM system and a commercial multidirectional LSFM system, clearly demonstrating that MDSR effectively removes most of the stripe artifacts. Moreover, we performed a comparative experiment with the variational stationary noise remover and the wavelet-FFT methods and quantitatively analyzed the results with a peak signal-to-noise ratio, showing an improved noise removal when using the MDSR method.
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.