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Towards unsupervised segmentation in high-resolution medical nano-imaging

Dietlmeier, Julia orcid logoORCID: 0000-0001-9980-0910, Ghita, Ovidiu and Whelan, Paul F. orcid logoORCID: 0000-0002-2029-1576 (2011) Towards unsupervised segmentation in high-resolution medical nano-imaging. In: Bioengineering...in Ireland 17, 17th annual conference of the bioengineering section of the royal academy of medicine in Ireland, 28-29 Jan 2011, Galway.

Recent advances in cellular and subcellular microscopy demonstrated its potential towards unraveling the mechanisms of various diseases at the molecular level. From a computer vision perspective nano-imaging is an inherently complex environment as can for example be seen from Fig.1(a,c). For the image analysis of intracellular organisms in high-resolution microscopy, new techniques which are capable of handling high-throughput data in a single pass and real time are of special interest. The additional emphasis is put therein on automated solutions which can provide the objective quantitative information in a reasonable time frame. The state-of-the-art is dominated by manual data annotation[1]and the early attempts to automate the segmentation are based on statistical machine-learning techniques[4].
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:computer vision; image analysis; Spectral clustering; Image segmentation; Dimensionality reduction; Latent variables
Subjects:Engineering > Electronic engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:18593
Deposited On:13 Aug 2013 13:05 by Mark Sweeney . Last Modified 13 Dec 2019 16:26

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