Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this thesis, a detection method is proposed for airport runways, which is the most distinguishing element of an airport. This method, which operates on large optical satellite images, is composed of a segmentation process based on textural properties, and a runway shape detection stage. In the segmentation process, several local textural features are extracted including not only low level features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis. Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other structures and landforms, cannot be predicted, Adaboost learning algorithm is employed for both classification and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a coarse representation of possible runway locations is obtained, as a result of the segmentation operation. Subsequently, the runway shape detection stage, based on a novel form of Hough Transform, is performed over the possible runway locations, in order to obtain final runway positions. The proposed algorithm is examined with experimental work using a comprehensive data set consisting of large and high resolution satellite images and successful results are achieved.

In this study, an automated building extraction system, which is capable of detecting buildings from satellite images using only RGB color band is implemented. The approach used in this work has four main steps: local feature extraction, feature selection, classification and post processing. There are many studies in literature that deal with the same problem. The main issue is to find the most suitable features to distinguish a building. This work presents a feature selection scheme that is connected with the classification framework of Adaboost. As well as Adaboost, four SVM kernels are used for classification. Detailed analysis regarding window type and size, feature type, feature selection, feature count and training set is done for determining the optimal parameters for the classifiers. A detailed comparison of SVM and Adaboost is done based on pixel and object performances and the results obtained are presented both numerically and visually. It is observed that SVM performs better if quadratic kernel is used than the cases using linear, RBF or polynomial kernels. SVM performance is better if features are selected either by Adaboost or by considering errors obtained on histograms of features. The performance obtained by quadratic kernel SVM operated on Adaboost selected features is found to be 38% in terms of pixel based performance criteria quality percentage and 48% in terms object based performance criteria correct detection with building detection threshold 0.4. Adaboost performed better than SVM resulting in 43% quality percentage and 67% correct detection with the same threshold.

Introduction: A computer-based classification system is proposed for the characterization of cervical intervertebral disc degeneration from saggital magnetic resonance images. Materials and methods: Cervical intervertebral discs from saggital magnetic resonance images where assessed by an experienced orthopaedist as normal or degenerated (narrowed) employing Matsumoto’s classification scheme. The digital images where enhanced and the intervertebral discs which comprised the regions of interest were segmented. First and second order statistics textural features extracted from thirty-four discs (16 normal and 16 degenerated) were used in order to design and test the classification system. In addition textural features were calculated employing Laws TEM images. The existence of statistically significant differences between the textural features values that were generated from normal and degenerated discs was verified employing the Student’s paired t-test. A subset with the most discriminating features (p<0.01) was selected and the Exhaustive Search and Leave-One-Out methods were used to find the best features combination and validate the classification accuracy of the system. The proposed system used the Least Squares Minimum Distance Classifier in combination with four textural features with comprised the best features combination in order to classify the discs as normal or degenerated. Results: The overall classification accuracy was 93.8% misdiagnosing 2 discs. In addition the system’s sensitivity in detecting a narrow disc was 93.8% and its specificity was also 93.8%. Conclusion: Further investigation and the use of a larger sample for validation could make the proposed system a trustworthy and useful tool to the physicians for the evaluation of degenerative disc disease in the cervical spine. / Σκοπός: Η στένωση των μεσοσπονδύλιων δίσκων της αυχενικής μοίρας, ως κύρια έκφραση εκφυλιστικής νόσου, είναι μια από τις σημαντικότερες αιτίες πρόκλησης πόνου στην περιοχή του αυχένα. Στην κλινική πράξη η αξιολόγηση της στένωσης γίνεται μέσω μέτρησης του μεσοσπονδύλιου διαστήματος, σε διάφορες απεικονίσεις της αυχενικής μοίρας του ασθενούς. Στην παρούσα εργασία προτείνεται μια υπολογιστική μέθοδος ανάλυσης εικόνας, για την αυτοματοποιημένη εκτίμηση της στένωσης από εικόνες μαγνητικής τομογραφίας. Υλικό και Μέθοδος: Μελετήθηκαν 34 μεσοσπονδύλιοι δίσκοι από οβελιαίες τομές μαγνητικής τομογραφίας της αυχενικής μοίρας, οι οποίες ελήφθησαν με χρήση Τ2 ακολουθίας. Η στένωση των μεσοσπονδύλιων δίσκων αξιολογήθηκε από έμπειρο ορθοπαιδικό βάσει της κλίμακας Matsumoto. Οι δίσκοι χωρίστηκαν σε δύο κατηγορίες: (α) 16 φυσιολογικοί και (β) 16 δίσκοι που παρουσίαζαν στένωση. Με χρήση διαδραστικού περιβάλλοντος επεξεργασίας εικάνας καθορίστηκε το περίγραμμα των μεσοσπονδύλιων δίσκων οι οποίοι αποτελούν τις προς ανάλυση περιοχές ενδιαφέροντος (Π.Ε.). Σε κάθε Π.Ε. εφαρμόστηκαν αλγόριθμοι εξαγωγής χαρακτηριστικών υφής. Συγκεκριμένα υπολογίστικαν χαρακτηριστικά υφής από στατιστικά πρώτης και δεύτερης τάξης καθώς και χαρακτηριστικά από τα μέτρα ενέργειας υφλης κατλα Laws. Τα παραπάνω χαρακτηριστικά, ποσοτικοποιούν διαγνωστικές πληροφορίες της έντασης του σήματος της Π.Ε. και συσχετίζονται με τη βιοχημική σύσταση των απεικονιζόμενων δομών. Τα εξαχθέντα χαρακτηριστικά υφής αξιοποιήθηκαν για τη σχεδίαση του ταξινομητή ελάχιστης απόστασης ελαχίστων τετραγώνων, ο οποίος χρησιμοποιήθηκε για το διαχωρισμό μεταξύ φυσιολογικών δίσκων και δίσκων που παρουσίαζαν στένωση (εκφυλισμένων). Αποτελέσματα: Η ακρίβεια της ταξινόμησης φυσιολογικών και εκφυλισμένων μεσοσπονδύλιων δίσκων ανήλθε σε 93.8%. Η ευαισθησία καθώς και η ειδικότητα της μεθόδου, σε ότι αφορά την ανίχνευση εκφυλισμένων δίσκων, είναι επίσης 93.8%. Συμπέρασμα: Με δεδομένο το μικρό μέγεθος του δείγματος που χρησιμοποιήθηκε για το σχεδιασμό της μεθόδου, απαιτούνται περετέρω εργασίες πιστοποίησης της ακρίβειας ταξινόμησης, προκειμένου η μέθοδος αυτή να αξιοποιηθεί από ακτινολόγους και ορθοπαιδικους, ως βοηθητικό διαγνωστικό εργαλείο.

The master thesis is focused on creating a methodology for quantification of the nerve fiber layer on photographs of the retina. The introductory part of the text presents a medical motivation of the thesis and mentions several studies dealing with this issue. Furthermore, the work describes available textural features and compares their ability to quantify the thickness of the nerve fiber layer. Based on the described knowledge, the methodology to make different regression models enabling prediction of the retinal nerve fiber layer thickness was developed. Then, the methodology was tested on the available image dataset. The results showed, that the outputs of regression models achieve a high correlation between the predicted output and the retinal nerve fiber layer thickness measured by optical coherence tomography. The conclusion discusses an usability of the applied solution.