Literature DB >> 17282170

Frames-Based Denoising in 3D Confocal Microscopy Imaging.

Ioannis Konstantinidis1, Alberto Santamaria-Pang, Ioannis Kakadiaris.   

Abstract

In this paper, we propose a novel denoising method for 3D confocal microscopy data based on robust edge detection. Our approach relies on the construction of a non-separable frame system in 3D that incorporates the Sobel operator in dual spatial directions. This multidirectional set of digital filters is capable of robustly detecting edge information by ensemble thresholding of the filtered data. We demonstrate the application of our method to both synthetic and real confocal microscopy data by comparing it to denoising methods based on separable 3D wavelets and 3D median filtering, and report very encouraging results.

Year:  2005        PMID: 17282170     DOI: 10.1109/IEMBS.2005.1616401

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  2 in total

Review 1.  Automated reconstruction of neuronal morphology: an overview.

Authors:  Duncan E Donohue; Giorgio A Ascoli
Journal:  Brain Res Rev       Date:  2010-11-27

2.  Denoising for 3-d photon-limited imaging data using nonseparable filterbanks.

Authors:  Alberto Santamaria-Pang; Teodor Stefan Bildea; Shan Tan; Ioannis A Kakadiaris
Journal:  IEEE Trans Image Process       Date:  2008-12       Impact factor: 10.856

  2 in total

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