Literature DB >> 15360932

Automatic segmentation of digital micrographs: a survey.

Tim W Nattkemper1.   

Abstract

Digital micrographs and play a crucial role in today's bio-medical research. Due to progresses in experiment standardization and automation large sets of digital microscopy images, so called micrographs are recorded and stored to databases. The subsequent analysis of the large number of digital images needs the image information to be transformed into quantitative data, which can be processed by statistical methods and datamining. This article summarizes applications of optical microscopy in biomedical research and describes the individual characteristics in micrograph segmentation and classification. An overview on past works based on image processing and artificial neural networks is given and the problem of segmentation evaluation. It concludes with recommendations for future works.

Mesh:

Year:  2004        PMID: 15360932

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  6 in total

1.  Histology image analysis for carcinoma detection and grading.

Authors:  Lei He; L Rodney Long; Sameer Antani; George R Thoma
Journal:  Comput Methods Programs Biomed       Date:  2012-03-20       Impact factor: 5.428

2.  Workflow and methods of high-content time-lapse analysis for quantifying intracellular calcium signals.

Authors:  Fuhai Li; Xiaobo Zhou; Jinmin Zhu; Weiming Xia; Jinwen Ma; Stephen T C Wong
Journal:  Neuroinformatics       Date:  2008-05-28

3.  Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

Authors:  Christian Held; Tim Nattkemper; Ralf Palmisano; Thomas Wittenberg
Journal:  J Pathol Inform       Date:  2013-03-30

4.  High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles.

Authors:  Fuhai Li; Xiaobo Zhou; Jinmin Zhu; Jinwen Ma; Xudong Huang; Stephen T C Wong
Journal:  BMC Biotechnol       Date:  2007-10-09       Impact factor: 2.563

5.  Open source bioimage informatics for cell biology.

Authors:  Jason R Swedlow; Kevin W Eliceiri
Journal:  Trends Cell Biol       Date:  2009-10-14       Impact factor: 20.808

6.  3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells.

Authors:  Tong Luo; Huan Chen; Ghassan S Kassab
Journal:  PLoS One       Date:  2016-02-16       Impact factor: 3.240

  6 in total

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