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Analysis of Neural Data

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Semi-Automatic Quality Determination of 3D Confocal Microscope Scans of Neuronal Cells Denoised by 3D-Wavelet Shrinkage
Citation key Dima1999a
Author Dima, A. and Scholz, M. and Obermayer, K.
Title of Book Wavelet Applications VI - Proceedings of the SPIE
Pages 446 – 457
Year 1999
DOI 10.1117/12.342957
Editor Szu H. H.
Abstract The main goal of this work is to denoise 3D confocal microscope scans of neuronal cells taken at high resolution such that neuronal structures of size smaller than $1 \mu m$ become visible. Although scanning confocal microscopes give much clearer images than ordinary light microscopes do, the images are still noisy and blurred. Our goal is to filter out the noise in these images without disturbing the smallest neuronal structures which have the same signal amplitude and geometric size as the noise. In order to obtain a good scale-space representation of the analyzed image, we use the 3D-wavelet transformation. We extend the denoising method of Donoho \citeDono95b for 3D data and obtain several ways of computing thresholds and noise variances. Finally we develop a quality measure, for images with tree like structures, to determine the denoising method and wavelet form best suited for a particular confocal scan.
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