The image analysis techniques presented in the présent thesis have been developed as part of a European projeet dedicated to the development of an automatic membrane protein crystallization pipeline. A large number of samples is simultaneously produced and assessed by transmission electron microscope (TEM) screening. Automating this fast step implicates an on-fine analysis of acquired images to assure the microscope control by selecting the regions to be observed at high magnification and identify the components for specimen characterization.The observation of the sample at medium magnification provides the information that is essential to characterize the success of the 2D crystallization. The resulting objects, and especially the artificial membranes, are identifiable at this scale. These latter present only a few characteristic signatures, appearing in an extremely noisy context with gray-level fluctuations. Moreover they are practically transparent to electrons yielding low contrast. This thesis presents an ensemble of image processing techniques to analyze medium magnification images (5-15 nm/pixel). The original contribution of this work lies in: i) a statistical evaluation of contours by measuring the correlation between gray-levels of neighbouring pixels to the contour and a gradient signal for over-segmentation reduction, ii) the recognition of foreground entities of the image and iii) an initial study for their classification. This chain has been already tested on-line on a prototype and is currently evaluated.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00559800 |
| Date | 25 November 2009 |
| Creators | Karathanou, Argyro |
| Publisher | Université de Haute Alsace - Mulhouse |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
Page generated in 0.0021 seconds