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.

Many practical applications in the field of medical image processing require valid and reliable segmentation of images. In this dissertation, we propose three different semi-automatic segmentation frameworks for 2D-upper torso medical images to construct 3D geometric model of the torso structures. In the first framework, an extended version of the Otsu&rsquo / s method for three level thresholding and a recursive connected component algorithm are combined. The segmentation process is accomplished by first using Extended Otsu&rsquo / s method and then labeling in each consecutive slice. Since there is no information about pixel positions in the outcome of Extended Otsu&rsquo / s method, we perform some processing after labeling to connect pixels belonging with the same tissue. In the second framework, Chan-Vese (CV) method, which is an example of active contour models, and a recursive connected component algorithm are used together. The segmentation process is achieved using CV method without egde information as stopping criteria. In the third and last framework, the combination of watershed transformation and K-means are used as the segmentation method. After segmentation operation, the labeling is performed for the determination of the medical structures. In addition, segmentation and labeling operation is realized for each consecutive slice in each framework. The results of each framework are compared quantitatively with manual segmentation results to evaluate their performances.

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.

Images are liable to being corrupted by noise when they are processed for many applications such as sampling, storage and transmission. In this thesis, we propose a method of image restoration for image corrupted by a white Gaussian noise. This method is based upon Gauss-Markov random field model combined with a technique of image segmentation. As a result, the image can be restored by MAP estimation. In the approach of Gauss-Markov random field model, the image is restored by MAP estimation implemented by simulated annealing or deterministic search methods. By image segmentation, the region parameters and the power of generating noise can be obtained for every region. The above parameters are important for MAP estimation of the Gauss-Markov Random field model. As a summary, we first segment the image to find the important region parameters and then restore the image by MAP estimation with using the above region parameters. Finally, the intermediate image is restored again by the conventional Gauss-Markov random field model method. The advantage of our method is the clear edges by the first restoration and deblured images by the second restoration.

The restoration of degraded images is one important application of image processing. The classical approach of image restoration, such as low-pass filter method, is usually stressed on the numerical error but with a disadvantage in visual quality of blurred texture. Therefore, a new method of image restoration, based upon image model by Compound Gauss-Markov(CGM) Random Fields, using MAP(maximum a posteriori probability) approach focused on image texture effect has been proved to be helpful. However, the contour of the restored image and numerical error for the method is poor because the conventional CGM model uses fixed global parameters for the whole image. To improve these disadvantages, we adopt the adjustable parameters method to estimate model parameters and restore the image. But the parameter estimation for the CGM model is difficult since the CGM model has 80 interdependent parameters. Therefore, we first adopt the parameter reduction approach to reduce the complexity of parameter estimation. Finally, the initial value set of the parameters is important. The different initial value might produce different results. The experiment results show that the proposed method using adjustable parameters has good numerical error and visual quality than the conventional methods using fixed parameters.

This Compound Gauss-Markov image model has been proven helpful in image restoration. In this model, a pixel in the image random field is determined by the surrounding pixels according to a predetermined line field. In this thesis, we restored the noisy image based upon the traditional Compound Gauss-Markov image field without the constraint of the model parameters introduced in the original work. The image is restored in two steps iteratively: restoring the line field by the assumed image field and restoring the image field by the just computed line field. Two methods are proposed to replace the traditional method in solving for the line field. They are probability method and vector method. In probability method, we break away from the limitation of the energy function Vcl(L) and the mystical system parameters Ckll(m,n) and£mw2. In vector method, the line field appears more reasonable than the original method. The image restored by our methods has a similar visual quality but a better numerical value than the original method.

Craniosynostosis is the premature fusion of one or more cranial sutures in the developing skull. If left untreated, craniosynostosis can result in developmental delays, blindness, deafness, and other impairments resulting from an increase in the intracranial pressure. In many cases, the treatment consists of complex calvarial vault reconstruction with the hope of restoring a normal skull appearance and volume. Re-synostosis, the premature re-closure following surgery, occurs in up to 40% children who undergo surgery. If this occurs, a second surgery is needed to remove portions of the fused skull in an attempt to correct the deformities and/or relieve an increase in intracranial pressure. These subsequent surgeries are associated with an incredibly high incidence of life threatening complications. To address this unmet clinical need we have developed strategies to delay the post-operative bone growth in a clinically relevant murine model of re-synostosis. The overall objective of this thesis was to develop a hydrogel based therapy to delay rapid bone regeneration in a murine model of re-synostosis. The overall hypothesis was that delivery of key BMP inhibitors involved in regulating normal suture development and regeneration will delay the rapid bone growth that in seen in a pediatric murine model of re-synostosis. The overall approach is to use micro-computed tomography (µCT) to determine the time course of suture fusion and to identify genes associated with key developmental time points, to develop a pediatric specific mouse model that displays rapid re-synostosis, and lastly to develop a hydrogel based therapy to delay the re-synostosis of this cranial defect.

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.

Image-guided therapies have the potential to improve the accuracy of treating liver cancer. In order to register intraoperative with preoperative liver images, joint segmentation and registration methods require fast segmentation of matching vessel centerlines. The algorithm presented in this thesis solves this problem by tracking the centerlines using ridge and cross-section information, and uses knowledge of the patient’s vasculature in the preoperative image to ensure correspondence. The algorithm was tested on three MR images of healthy volunteers and one CT image of a patient with liver cancer. Results show that in the context of join segmentation registration, if the registration error is less than 2.0mm, the average segmentation error is 0.73-1.68mm, with 88-100% of the vessels having an error less than a voxel length. For registration error less than 4.6mm, the average segmentation error is 1.17-2.11mm, with 79-98% of the vessels having an error less than a voxel length.