eng URI http://hdl.handle.net/10795/3287 σύστημα πληροφορικής εφαρμογή της πληροφορικής επεξεργασία δεδομένων ιατρικές επιστήμες This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours. The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization. The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics. 188 pp. LOMv1.0 creator LOMv1.0educational validator LOMv1.0 Scientific Coordinator LOMv1.0 Project Executing Organisation 2014-01 Digital Library of the Operational Programme "Education and Lifelong Learning" abstract types Text Segmentation Active Contours Proteomics Images Medical Images Κατάτμηση Ενεργά Περιγράμματα Εικόνες Πρωτεομικής Ιατρικές Εικόνες 6741734 application/pdf http://repository.edulll.gr/edulll/bitstream/10795/3287/2/3287_1.154_%ce%94%ce%94_9_1_14.pdf Περιλαμβάνεται ελληνική περίληψη URI http://hdl.handle.net/10795/3287 LOMv1.0creator 2016-05-19T13:12:38Z LOMv1.0validator 2016-05-19T13:12:38Z LOMv1.0 gre no no Copyright EYD-EPEDBM (Operational Programme "Education and Lifelong Learning")