Many research questions in biological research lead to numerous microscope images that need to be evaluated. Here digital image cytometry, i.e., quantitative, automated or semi-automated analysis of the images is an important rapidly growing discipline. This thesis presents contributions to that field. The work has been carried out in close cooperation with biomedical research partners, successfully solving real world problems. The world is 3D and modern imaging methods such as confocal microscopy provide 3D images. Hence, a large part of the work has dealt with the development of new and improved methods for quantitative analysis of 3D images, in particular fluorescently labeled skeletal muscle cells. A geometrical model for robust segmentation of skeletal muscle fibers was developed. Images of the multinucleated muscle cells were pre-processed using a novel spatially modulated transform, producing images with reduced complexity and facilitating easy nuclei segmentation. Fibers from several mammalian species were modeled and features were computed based on cell nuclei positions. Features such as myonuclear domain size and nearest neighbor distance, were shown to correlate with body mass, and femur length. Human muscle fibers from young and old males, and females, were related to fiber type and extracted features, where myonuclear domain size variations were shown to increase with age irrespectively of fiber type and gender. A segmentation method for severely clustered point-like signals was developed and applied to images of fluorescent probes, quantifying the amount and location of mitochondrial DNA within cells. A synthetic cell model was developed, to provide a controllable golden standard for performance evaluation of both expert manual and fully automated segmentations. The proposed method matches the correctness achieved by manual quantification. An interactive segmentation procedure was successfully applied to treated testicle sections of boar, showing how a common industrial plastic softener significantly affects testosterone concentrations.

Technologies in image acquisition have developed and often provide image volumes in more than two dimensions. Computer tomography and magnet resonance imaging provide image volumes in three spatial dimensions. The image enhancement methods have developed as well and in this thesis work 3D image enhancement with filter networks is evaluated. The aims of this work are; to find a method which makes the initial parameter settings in the 3D image enhancement processing easier, to compare 2D and 3D processed image volumes visualized with different visualization techniques and to give an illustration of the benefits with 3D image enhancement processing visualized using these techniques. The results of this work are; 1. a parameter setting tool that makes the initial parameter setting much easier and 2. an evaluation of 3D image enhancement with filter networks that shows a significant enhanced image quality in 3D processed image volumes with a high noise level compared to the 2D processed volumes. These results are shown in slices, MIP and volume rendering. The differences are even more pronounced if the volume is presented in a different projection than the volume is 2D processed in.

Medical imaging modalities provide effective information for anatomic or metabolic activity of tissues and organs in the body. Therefore, medical imaging technology is a critical component in diagnosis and treatment of various illnesses. Medical image segmentation plays an important role in converting medical images into anatomically, functionally or surgically identifiable structures, and is used in various applications. In this study, some of the major medical image segmentation methods are examined and applied to 2D CT images of upper torso for segmentation of heart, lungs, bones, and muscle and fat tissues. The implemented medical image segmentation methods are thresholding, region growing, watershed transformation, deformable models and a hybrid method / watershed transformation and region merging. Moreover, a comparative analysis is performed among these methods to obtain the most efficient segmentation method for each tissue and organ in torso. Some improvements are proposed for increasing accuracy of some image segmentation methods.

Medical image analysis is one of the most critical studies in field of medicine, since results gained by the analysis guide radiologists for diagnosis, treatment planning, and verification of administered treatment. Therefore, accuracy in analysis of medical images is at least as important as accuracy in data acquisition processes. Medical images require sequential application of several image post-processing techniques in order to be used for quantification and analysis of intended features. Main objective of this thesis study is to build up an application framework, which enables analysis and quantification of several features in medical images with minimized input-dependency over results. Intended application targets to present a software environment, which enables sequential application of medical image processing routines and provides support for radiologists in diagnosis, treatment planning and treatment verification phases of neurodegenerative diseases and brain tumors / thus, reducing the divergence in results of operations applied on medical images. In scope of this thesis study, a comprehensive literature review is performed, and a new medical image processing and analysis framework - including modules responsible for automation of separate processes and for several types of measurements such as real tumor volume and real lesion area - is implemented. Performance of the fully-automated segmentation module is evaluated with standards introduced by Neuro Imaging Laboratory, UCLA / and the fully-automated registration module with Normalized Cross-Correlation metric. Results have shown a success rate above 90 percent for both of the modules. Additionally, a number of experiments have been designed and performed using the implemented application. It is expected for an accurate, flexible, and robust software application to be accomplished on the basis of this thesis study, and to be used in field of medicine as a contributor by even non-engineer professionals.

Medical image segmentation is a challenging problem, and is studied widely. In this thesis, the main goal is to develop automatic segmentation techniques of human mimic muscles and to compare them with ground truth data in order to determine the method that provides best segmentation results. The segmentation methods are based on Bayesian with Markov Random Field (MRF) and Level Set (Active Contour) models. Proposed segmentation methods are multi step processes including preprocess, main muscle segmentation step and post process, and are applied on three types of data: Magnetic Resonance Imaging (MRI) data, Computerized Tomography (CT) data and unified data, in which case, information coming from both modalities are utilized. The methods are applied both in three dimensions (3D) and two dimensions (2D) data cases. A simulation data and two patient data are utilized for tests. The patient data results are compared statistically with ground truth data which was labeled by an expert radiologist.

Medical imaging tools technologically supported by the recent advances in the areas of computer vision can provide systems that aid medical professionals to carry out their expert diagnostics and investigations more effectively and efficiently. Two medical application domains that can benefit by such tools are ophthalmology and Total Hip Replacement (THR). Although a literature review conducted within the research context of this thesis revealed a number of existing solutions these are either very much limited by their application scope, robustness or scope of the extensiveness of the functionality made available. Therefore this thesis focuses on initially investigating a number of requirements defined by leading experts in the respective specialisms and providing practical solutions, well supported by the theoretical advances of computer vision and pattern recognition. This thesis provides three novel algorithms/systems for use within image analysis in the areas of Ophthalmology and THR. The first approach uses Contourlet Transform to analyse and quantify corneal neovascularization. Experimental results are provided to prove that the proposed approach provides improved robustness in the presence of noise, non-uniform illumination and reflections, common problems that exist in captured corneal images. The second approach uses a colour based segmentation approach to segment, measure and analyse corneal ulcers using the HVS colour space. Literature review conducted within the research context of this thesis revealed that there is no such system available for analysis and measurement of corneal ulcers. Finally the thesis provides a robust approach towards detecting and analysing possible dislocations and misalignments in THR X-ray images. The algorithm uses localised histogram equalisation to enhance the quality of X-ray images first prior to using Hough Transforms and filtered back projections to locate and recognise key points of the THR x-ray images. These key points are then used to measure the possible presence of dislocations and misalignments. The thesis further highlights possible extensions and improvements to the proposed algorithms and systems.

Στα πλαίσια της παρούσης εργασίας πραγματοποιήθηκε μελέτη της μεθόδου Συνόλων Επιπέδου για την κατάτμηση καροτίδων από τρισδίαστατες εικόνες. Ειδικότερα πραγματοποιήθηκε μελέτη των παθολογιών που συνδέονται με αυτές προκειμένου να καταστούν εμφανή τα κίνητρα της παρούσης εργασίας, όσον αφορά στη συμβολή της στην κλινική σημασία και ιατρική πρακτική. Κατ’αυτόν τον τρόπο, αφού παρουσιάστηκε η ανατομία των καροτίδων και οι δυσκολίες που ενέχει το εγχείρημα της κατάτμησής τους καθώς και μια ανασκόπηση των μεθόδων Συνόλων Επιπέδου (Level-Sets) για κατάτμηση ιατρικής εικόνας και δη καροτίδων, παρουσιάστηκε το γενικό μοντέλο και ο μαθηματικός φορμαλισμός της μεθόδου που χρησιμοποιήθηκε. Εν συνεχεία παρουσιάστηκαν τα τρισδιάστατα δεδομένα και η διαχείρησή τους, οι προγραμματιστικές διαπαφές και υποδομές με τις οποίες υλοποιήθηκαν δύο παραλλαγές της μεθόδου. Επίσης παρουσιάζονται τα αποτελέσματα της μεθόδου οπτικοποιημένα και τέλος συγκρίνονται με αντίστοιχα αποτελέσματα ενός ειδικού ακτινολόγου στη βάση κάποιων κατάλληλων μετρικών. Τέλος παρουσιάζονται τα συμπεράσματα που προέκυψαν καθώς και κάποιες ιδέες για μελλοντική δουλειάπου μπορεί να γίνει στη βάση αυτής που έγινε στα πλαίσια της εν λόγω μεταπτυχιακής διατριβής. / The present thesis outlines the methods we have developed for segmenting both normal and pathological carotid images, acquired with the Computed Tomography (CT) protocol. The layout of the thesis is the following: Chapter 2 analyses the methodological background of the current study. At first, section 2.1 provides an overview to the anatomy of carotids. Section 2.2 reviews the literature of segmentation methods based on level sets for medical images and at last reviews the level set methods developed for segmenting carotids. In addition, section 2.3 presents the conceptual model deployed in the current study, following with the analysis of the particular class we used. Next, section 2.4 treats of the level set method, presenting its basic derivation and furthermore discriminating between the two algorithms used according to their speed function. Chapter 3 refers to the materials and methods. It begins in section 3.1 with a description of the data provided for the experimental demonstration, and the programming interface by deployment of which the experimental procedure took place. Later on, in section 3.2 the implementation of the deployed methods in the programming interface used is presented with an analysis of their components. At last, all intermediate outputs and the final results of each method are illustrated. Chapter 4 presents the evaluation of the results of each method by comparison with a corresponding manual segmentation result on the basis of appropriate metrics. At last, refers to the conclusions occurred and to future work that can be carried out based on the current Msc thesis. In Appendix A some subsidiary methods, for the sake of a coherent flow are stated and analyzed independently.

In this work were studied, implemented and evaluated two algorithms of image registration with two similarity metrics of mutual information. These were Viola-Wells Mutual Information [6],[7] and Mattes Mutual Information[11]. Materials and Methods: Two 3D MRI T1 and Τ2 brain images were used. The T1 image was rotated in all three axes , with the 27 possible triples of angles 0.25, 1.5 and 3 degrees and in the T2 image were added 3 Gaussian Noise Levels (1,3,5%). Thus were formed two experiments. The monomodal experiment which was registering the initial T1 image with its 27 rotated instances and the multimodal experiment which was registering the 4 T2 images (0,1,3,5% Gaussian Noise) with the 27 rotated T1 images. The registration framework had also a Regular Step Gradient Descent Optimizer, affine linear transformation and linear interpolator. After the 5 experimental set were registered with both algorithms, then in order for the results to be evaluated, 5 similarity metrics were used. These were: 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Finally t-test was applied, in order to find statistically significant differences. Results: Both algorithms had similar outcome, although the algorithm with Mattes Μutual Information metric, had a slightly improved performance. Statistically important differences were found in the t-test. Conclusions: The two methods should be tested more, using other kinds of transformation, and more data sets. / Σε αυτήν την εργασία μελετήθηκαν, υλοποιήθηκαν και αξιολογήθηκαν δύο αλγόριθμοι αντιστοίχισης ιατρικών εικόνων με δύο μετρικές ομοιότητας με χρήση κοινού πληροφορίας. Συγκεκριμένα η υλοποίηση Viola-Wells [6],[7] και η υλοποίηση Mattes[11]. Υλικά και Μέθοδος: Χρησιμοποιήθηκαν δύο εικόνες 3D MRI T1 και Τ2 που απεικόνιζαν εγκέφαλου. Η εικόνα Τ1 περιστράφηκε με τους 27 δυνατές συνδυασμούς των γωνιών 0.25,1.5,3 μοιρών , σε όλους τους άξονες και στην εικόνα Τ2 προστέθηκαν 3 επίπεδα Gaussian θορύβου (1,3,5%). Έτσι σχηματίστηκαν δύο πειράματα. Το μονο-απεικονιστικό πείραμα (Monomodal) που αντιστοιχούσε την αρχική Τ1 εικόνα με τα 27 περιστρεμμένα στιγμιότυπα της και το πολύ-απεικονιστικό (multimodal) που αντιστοιχούσε τις 4 Τ2 εικόνες (0,1,3,5% Gaussian Noise) με τα 27 περιστρεμμένα στιγμιότυπα της Τ1. Το σχήμα της αντιστοίχισης αποτελούνταν εκτός από τις δύο μετρικές ομοιότητας, από τον Regular Step Gradient Descent βελτιστοποιητή , συσχετισμένο (affine) γραμμικό μετασχηματισμό και γραμμικό interpolator. Αφού τα 5 σύνολα πειραμάτων ταυτίστηκαν και με τους 2 αλγορίθμους στην συνέχεια και προκειμένου να αξιολογηθεί το αποτέλεσμα της αντιστοίχισης, χρησιμοποιήθηκαν 5 μετρικές ομοιότητας. Αυτές ήταν : 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Τέλος εφαρμόστηκε και t-test προκειμένου να επιβεβαιωθούν στατιστικώς σημαντικές διαφορές. Αποτελέσματα: Και οι δύο αλγόριθμοι βρέθηκαν να έχουν παρόμοια συμπεριφορά, ωστόσο ο αλγόριθμος που χρησιμοποιούσε την Mattes Μutual Information μετρική ομοιότητας είχε καλύτερα αποτελέσματα. Στατιστικώς σημαντικές διαφορές επιβεβαιώθηκαν και από το t-test. Συμπέρασμα: Οι δύο μέθοδοι θα πρέπει να αξιολογηθούν χρησιμοποιώντας και άλλους μετασχηματισμούς, καθώς και διαφορετικά data set.

Στο πρώτο και δεύτερο κεφάλαιο γίνεται μια λεπτομερή ανάλυση της ανατομίας και φυσιολογίας του ανθρώπινου οφθαλμού ενώ στο τρίτο και τέταρτο γίνεται αναφορά στην ανατομία του βρογχικού δένδρου, μια εισαγωγή στην ένοια της επεμβατικής πνευμονολογίας καθώς και μια εκτενής ανάλυση για τις μεθόδους βρογχοσκόπισης με ιδιέταιρη έμφαση στην μεθόδο αυτοφθορισμού για την ανίχνευση πρώιμου πνευμονικού καρκίνου. Στο πέμπτο κεφάλαιο γίνεται μια εισαγωγή στην επεξεργασία ψηφιακής εικόνας. Γίνεται μια αναφορά στους τρόπους επεξεργασίας εικόνας, καταλήγοντας στην ειδική μέθοδο επεξεργασίας ψηφιακής εικόνας που είναι η τμηματοποίηση. Στο έκτο κεφάλαιο γίνεται ορισμός της τμηματοποίησης ψηφιακής εικόνας. Οι διάφορες μέθοδοι που αναλύονται εκτενώς είναι : 1. Εφαρμογή κατωφλίου 2. Ανίχνευση ασυνεχειών 3. Region-based τμηματοποίηση 4. Edge linking and boundary detection 5. Μορφολογικά watersheds Τελειώνοντας το έκτο κεφάλαιο γίνεται αναφορά σε εξειδικευμένες εφαρμογές σύγχρονων μεθόδων για οφθαλμιατρικές και εικόνες video-βρογχοσκοπίου. Ελπίζω να έχω αποδώσει σωστά το αντικείμενο της εργασίας ώστε να χρησιμοποιηθεί ως βοήθημα σε όποιον επιθυμεί να εμβαθύνει στους τόμεις που αναφέρομαι. Το πνεύμα της εργασίας είναι να οδηγήσει στην κατανόηση, όχι να διδάξει / The project can introduce us in the field of medical image processing. In the first and the second section there is a detailed reference about the anatomy and fusiology of the retina. In the third and fourth you can find information about bronchous anatomy and general methods for bronchoscopy. In the fifth there are several methods for image processing and especialy the segmentation method. In the sixth section there is an analysis of the segmentation method and finaly there are some examples of the above methods.

Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes. This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches. / Acknowledgements page removed per author's request, 01/06/2014.