Autoadaptive Algorithm for the Stacking-Level Estimation of Membranes in TEM Images | Zendy

Gilles Hermann | Zendy; Nicolas Coudray | Zendy; Argyro Karathanou | Zendy; J.L. Buessler | Zendy; Jean-Philippe Urban | Zendy

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Autoadaptive Algorithm for the Stacking-Level Estimation of Membranes in TEM Images

Author(s) -

Gilles Hermann,

Nicolas Coudray,

Argyro Karathanou,

J.L. Buessler,

Jean-Philippe Urban

Publication year - 2011

Publication title -

isrn signal processing

Language(s) - English

Resource type - Journals

eISSN - 2090-505X

pISSN - 2090-5041

DOI - 10.5402/2011/650546

Subject(s) - stacking , image (mathematics) , computer science , membrane , artificial intelligence , algorithm , noise (video) , task (project management) , computer vision , pattern recognition (psychology) , chemistry , engineering , biochemistry , organic chemistry , systems engineering

This paper introduces an original algorithm for the labeling of the regions of a partitioned image according to the stacking level of membranes in transmission electron microscopy (TEM) images. Image analysis of membrane protein TEM images represents a particular challenging task because of the important noise and heterogeneity present in these images. The proposed algorithm adapts automatically to fluctuations and gray level ranges characterizing each membrane stacking level. Some informationabout the organization of the objects in the images is introduced as prior knowledge. Three types of qualitative and quantitativeexperiments have been specifically devised and implemented to assess the algorithm.

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