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Automatic macroscopic density artefact removal in a Nissl‐stained microscopic atlas of whole mouse brain
Author(s) -
DING W.,
LI A.,
WU J.,
YANG Z.,
MENG Y.,
WANG S.,
GONG H.
Publication year - 2013
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12058
Subject(s) - nissl body , tracing , visualization , artificial intelligence , computer science , preprocessor , computer vision , brain atlas , segmentation , rendering (computer graphics) , image quality , microscopy , brightness , voxel , data set , grayscale , image processing , staining , pathology , image (mathematics) , optics , physics , medicine , operating system
Summary Acquiring a whole mouse brain at the micrometer scale is a complex, continuous and time‐consuming process. Because of defects caused by sample preparation and microscopy, the acquired image data sets suffer from various macroscopic density artefacts that worsen the image quality. We have to develop the available preprocessing methods to improve image quality by removing the artefacts that effect cell segmentation, vascular tracing and visualization. In this study, a set of automatic artefact removal methods is proposed for images obtained by tissue staining and optical microscopy. These methods significantly improve the complicated images that contain various structures, including cells and blood vessels. The whole mouse brain data set with Nissl staining was tested, and the intensity of the processed images was uniformly distributed throughout different brain areas. Furthermore, the processed image data set with its uniform brightness and high quality is now a fundamental atlas for image analysis, including cell segmentation, vascular tracing and visualization.