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10.1111/jmi.12281 http://scihub22266oqcxt.onion/10.1111/jmi.12281 C4619154!4619154 !26224257     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26224257 &cmd=llinks): Failed to open stream: HTTP request failed! 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A Model-based approach for microvasculature structure distortion correction in two-photon fluorescence microscopy images |pdf=|usr=}}{{26224257 }} gab.com Text A Model-based approach for microvasculature structure distortion correction in two-photon fluorescence microscopy images JO: J Microsc http://www.kidney.de/pget.php?&tw=1&pmid=26224257 #FOAvip This paper investigates a postprocessing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modelling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to subvolumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional (3D) spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images. Twit Text FOAVip A Model-based approach for microvasculature structure distortion correction in two-photon fluorescence microscopy images ????J Microsc http://www.kidney.de/pget.php?&tw=1&pmid=26224257 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26224257 #FOAvip English Wikipedia <ref>{{Cite pmid|26224257 }}</ref> A Model-based approach for microvasculature structure distortion correction in two-photon fluorescence microscopy images #MMPMID26224257 Dao L ; Glancy B ; Lucotte B ; Chang LC ; Balaban RS ; Hsu LY J Microsc 2015[Nov]; 260 (2 ): 180-93 PMID26224257 show ga This paper investigates a postprocessing approach to correct spatial distortion in two-photon fluorescence microscopy images for vascular network reconstruction. It is aimed at in vivo imaging of large field-of-view, deep-tissue studies of vascular structures. Based on simple geometric modelling of the object-of-interest, a distortion function is directly estimated from the image volume by deconvolution analysis. Such distortion function is then applied to subvolumes of the image stack to adaptively adjust for spatially varying distortion and reduce the image blurring through blind deconvolution. The proposed technique was first evaluated in phantom imaging of fluorescent microspheres that are comparable in size to the underlying capillary vascular structures. The effectiveness of restoring three-dimensional (3D) spherical geometry of the microspheres using the estimated distortion function was compared with empirically measured point-spread function. Next, the proposed approach was applied to in vivo vascular imaging of mouse skeletal muscle to reduce the image distortion of the capillary structures. We show that the proposed method effectively improve the image quality and reduce spatially varying distortion that occurs in large field-of-view deep-tissue vascular dataset. The proposed method will help in qualitative interpretation and quantitative analysis of vascular structures from fluorescence microscopy images. |*Microscopy, Fluorescence/instrumentation/methods [MESH] |Algorithms [MESH] |Animals [MESH] |Artifacts [MESH] |Equipment Design [MESH] |Image Processing, Computer-Assisted/methods [MESH] |Imaging, Three-Dimensional/methods [MESH] |Intravital Microscopy/methods [MESH] |Mice [MESH] |Microvessels/*ultrastructure [MESH] |Models, Theoretical [MESH] |Muscle, Skeletal/blood supply/ultrastructure [MESH] |Phantoms, Imaging [MESH] |Photons [MESH]A Model-based approach for microvasculature structure distortion corre(epmc).pdf DeepDyve Pubget Overpricing