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 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.

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.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

Este trabalho apresenta como parte de resultados, uma ampla pesquisa que permitiu identificar os grupos de pesquisas mais importantes do mundo, os quais possuem em comum o processamento de imagens médicas, mais especificamente o processamento de imagens que busca a identificação de microcalcificações mamárias. O vasto levantamento, a seleção e organização culminou na reunião de mais de cem artigos, publicados nos mais importantes periódicos da área, que mostram claramente as formas utilizadas pelos grupos de pesquisa para apresentação dos resultados encontrados pelos seus algoritmos. Esses resultados devem auxiliar o médico no diagnóstico do câncer de mama. Demonstramos neste trabalho porque as técnicas utilizadas para apresentação dos resultados são insatisfatórias e propusemos um novo método de avaliação desses resultados. O método proposto no trabalho baseia-se no teste do X&sup2 (Qui-Quadrado), nas curvas ROC (Receiver Operating Characteristic) e no teste de concordância, que juntos permitem apresentar de forma clara e objetiva as relações entre verdadeiros positivos e falsos positivos, verdadeiros negativos e falsos negativos, sensibilidade e especificidade do algoritmo analisado. O novo método é preciso e tem bases estatísticas conhecidas pelos médicos e pelos pesquisadores, facilitando sua aceitação. / This work presents as part of results, a wide investigation that it allowed to identify the principal research groups of the world, which possess in common the processing of medical images, more specifically the processing of images that search for the identification of mammary microcalcifications. The vast collection, selection and organization culminated in the meeting of more than a hundred articles, published in the most important newspapers of the area, that show the forms used by the research groups to present the results found clearly by its algorithms. Those results should assist the doctor in the diagnosis of the breast cancer. We demonstrated in this work that the techniques used for presentation of the results are unsatisfactory and we proposed a new method of evaluation of those results. The proposed method bases on the test of the X&sup2 (Qui-square), in ROC curve (Receiver Operating Characteristic) and in the agreement test, that take together allow to present in a clear and objective way the relationships among true positive and false positive, true negative and false negative, sensibility and specificity of the analyzed algorithm. The new method is precise and has statistical bases known by the clinicians and researchers, facilitating its acceptance.

Turbulent blood flow is involved in the pathogenesis of several cardiovascular diseases. While it is known that turbulence is present in patients with obstructive disease in the major vessels, the magnitude and impact of turbulence in the normal heart and aorta is still relatively unexplored. Besides, existing analysis method of the blood flow is a labour intensive process and requires excessive amount of time. A method to automatically create hemodynamic atlases has been developed, using 4D Flow magnetic resonance imaging (MRI), a powerful tool to measure blood flow characteristics. The resulting atlases show the expected blood flow characteristics in the aorta for a group of similar subjects. Application of the method in healthy young and healthy old has shown significant differences in kinetic energy and turbulent kinetic energy in the aortic flow.

Microscopy image processing is an emerging and quickly growing field in medical imaging research area. Recent advancements in technology including higher computation power, larger and cheaper storage modules, and more efficient and faster data acquisition devices such as whole-slide imaging scanners contributed to the recent microscopy image processing research advancement. Most of the methods in this research area either focus on automatically process images and make it easier for pathologists to direct their focus on the important regions in the image, or they aim to automate the whole job of experts including processing and classifying images or tissues that leads to disease diagnosis. This dissertation is consisted of four different frameworks to process microscopy images. All of them include methods for segmentation either as the whole suggested framework or the initial part of the framework for future feature extraction and classification. Specifically, the first proposed framework is a general segmentation method that works on histology images from different tissues and segments relatively solid nuclei in the image, and the next three frameworks work on cervical microscopy images, segmenting cervical nuclei/cells. Two of these frameworks focus on cervical tissue segmentation and classification using histology images and the last framework is a comprehensive segmentation framework that segments overlapping cervical cells in cervical cytology Pap smear images. One of the several commonalities among these frameworks is that they all work at the region level and use different region features to segment regions and later either expand, split or refine the segmented regions to produce the final segmentation output. Moreover, all proposed frameworks work relatively much faster than other methods on the same datasets. Finally, proving ground truth for datasets to be used in the training phase of microscopy image processing algorithms is relatively time-consuming, complicated and costly. Therefore, I designed the frameworks in such a way that they set most (if not all) of the parameters adaptively based on each image that is being processed at the time. All of the included frameworks either do not depend on training datasets at all (first three of the four discussed frameworks) or need very small training datasets to learn or set a few parameters.

This thesis investigates if support vector machine classification is a suitable approach when performing automatic segmentation of knee cartilage using quantitative magnetic resonance imaging data. The data sets used are part of a clinical project that investigates if patients that have suffered recent knee damage will develop cartilage damage. Therefore the thesis also investigates if the segmentation results can be used to predict the clinical outcome of the patients. Two methods that perform the segmentation using support vector machine classification are implemented and evaluated. The evaluation indicates that it is a good approach for the task, but the implemented methods needs to be further improved and tested on more data sets before clinical use. It was not possible to relate the cartilage properties to clinical outcome using the segmentation results. However, the investigation demonstrated good promise of how the segmentation results, if they are improved, can be used in combination with quantitative magnetic resonance imaging data to analyze how the cartilage properties change over time or vary between knees.

The purpose is trying to classify the Liver Fibrosis stage using Gadoxetic Acid-EnhancedMR Images.  In the very beginning, a method proposed by one Korean group is being examined and trying to reproduce their result. However, the performance is not as impressive as theirs. Then, some gray-scale image feature extraction methods are used. Last but not least, the hottest method in recent years - ConvolutionNeural Network(CNN) was utilized. Finally, the performance has been evaluated in both methods. The result shows that with manual feature extraction, the Adaboost model works pretty well that AUC achieves 0.9. Besides, the AUC of ResNet-18 network - a deep learning architecture, can reach 0.93. Also, all the hyperparameters and training settings used on ResNet-18 can be transferred to ResNet-50/ResNet-101/InceptionV3 very well. The best model that can be obtained is ResNet-101which has an AUC of 0.96 - higher than all current publications for machine learning methods for staging liver fibrosis.