Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding-Reference-Cited by-同舟云学术

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Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding

Published:2017-04 Issue: Volume:282 Page:20-33
ISSN:0165-0270
Container-title:Journal of Neuroscience Methods
language:en
Short-container-title:Journal of Neuroscience Methods

Author:

Alegro Maryana,Theofilas Panagiotis,Nguy Austin,Castruita Patricia A.,Seeley William,Heinsen Helmut,Ushizima Daniela M.,Grinberg Lea T.

Funder

National Institute of Health (NIH)

Office of Science, Advanced Scientific Computing Research, of the U.S. Department of Energy

Ushizima's Early Career Research project

Office of Science of the U.S. Department of Energy

Publisher

Elsevier BV

Subject

General Neuroscience

Reference69 articles.

1. A parallel distributed-memory particle method enables acquisition-rate segmentation of large fluorescence microscopy images;Afshar;PLOS ONE,2016

2. The k-svd: an algorithm for designing of overcomplete dictionaries for sparse representation;Aharon;IEEE Trans. Sig. Process.,2006

3. The human locus coeruleus complex: an immunohistochemical and three dimensional reconstruction study;Baker;Exp. Brain Res.,1989

4. Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals;Bergeest;Med. Image Anal.,2012

5. Image segmentation using dictionary learning and compressed random features;Bull;Dig. Image Comput.: Techn. Appl,2014

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