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published_or_final_version / Public Administration / Master / Master of Public Administration

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.

Confocal microendoscopy is a promising new diagnostic imaging technique that is minimally invasive and provides in-vivo cellular-level images of tissue. In this study, we developed various image analysis techniques for ovarian cancer detection using the confocal microendoscope system. Firstly, we developed a technique for automatic classification of images based on focus, to prune out the out-of-focus images from the ovarian dataset. Secondly, we modified the texture analysis technique developed earlier to improve the stability of the textural features. The modified technique gives stable features and more consistent performance for ovarian cancer detection. Although confocal microendoscopy provides cellular-level resolution, it is limited by a small field of view. We present a fast technique for stitching the individual frames of the tissue to form a large mosaic. Such a mosaic will aid the physician in diagnosis, and also makes quantitative and statistical analysis possible on a larger field of view.

Sub-pixel image alignment estimation is desirable for co-registration of objects in multiple images to a common spatial reference and as alignment input to multi-image processing. Applications include super-resolution, image fusion, change detection, object tracking, object recognition, video motion tracking, and forensics.Information theoretical measures are commonly used for co-registration in medical imaging. The published methods apply Shannon's Entropy to the Joint Measurement Space (JMS) of two images. This work introduces into the same context a new set of statistical divergence measures derived from Fisher Information. The new methods described in this work are applicable to uncorrelated imagery and imagery that becomes statistically least dependent upon co-alignment. Both characteristics occur with multi-modal imagery and cause cross-correlation methods, as well as maximum dependence indicators, to fail. Fisher Information-based estimators, together as a set with an Entropic estimator, provide substantially independent information about alignment. This increases the statistical degrees of freedom, allowing for precision improvement and for reduced estimator failure rates compared to Entropic estimator performance alone.The new Fisher Information methods are tested for performance on real remotely-sensed imagery that includes Landsat TM multispectral imagery and ESR SAR imagery, as well as randomly generated synthetic imagery. On real imagery, the co-registration cost function is qualitatively examined for features that reveal the correct point of alignment. The alignment estimates agree with manual alignment to within manual alignment precision. Alignment truth in synthetic imagery is used to quantitatively evaluate co-registration accuracy. The results from the new Fisher Information-based algorithms are compared to Entropy-based Mutual Information and correlation methods revealing equal or superior precision and lower failure rate at signal-to-noise ratios below one.

High-precision optical systems are generally tested using interferometry, since it often is the only way to achieve the desired measurement precision and accuracy. Interferometers can generally measure a surface to an accuracy of one hundredth of a wave. In order to achieve an accuracy to the next order of magnitude, one thousandth of a wave, each error source in the measurement must be characterized and calibrated.Errors in interferometric measurements are classified into random errors and systematic errors. An approach to estimate random errors in the measurement is provided, based on the variation in the data. Systematic errors, such as retrace error, imaging distortion, and error due to diffraction effects, are also studied in this dissertation. Methods to estimate the first order geometric error and errors due to diffraction effects are presented.Interferometer phase modulation transfer function (MTF) is another intrinsic error. The phase MTF of an infrared interferometer is measured with a phase Siemens star, and a Wiener filter is designed to recover the middle spatial frequency information.Map registration is required when there are two maps tested in different systems and one of these two maps needs to be subtracted from the other. Incorrect mapping causes wavefront errors. A smoothing filter method is presented which can reduce the sensitivity to registration error and improve the overall measurement accuracy.Interferometric optical testing with computer-generated holograms (CGH) is widely used for measuring aspheric surfaces. The accuracy of the drawn pattern on a hologram decides the accuracy of the measurement. Uncertainties in the CGH manufacturing process introduce errors in holograms and then the generated wavefront. An optimal design of the CGH is provided which can reduce the sensitivity to fabrication errors and give good diffraction efficiency for both chrome-on-glass and phase etched CGHs.

Physicians often utilize different imaging techniques to provide clear, visual information about internal parts of the patient. Since the different imaging modalities give different types of information, the combination of them serves as a powerful tool while determining the diagnosis, planning of treatment or during therapy follow-up. To simplify the interpretation of the image information, image registration is often used. The goal of the registration is to put different images in a common coordinate system. It is essential that the registration between the images is accurate. Normalized Mutual Information (NMI) is a metric that quantifies the conformity between images. Even though NMI is a robust method it is often dominated by large structures as the external contour of the patient as well as by the structures of the bones. The prostate is an organ that does not have a fixed position relative to the other organs and host small amounts of image information. The accuracy of the registration is therefore limited with respect to the prostate when using the whole image volume. This master thesis investigates the possibility to restrict the part of the image used for registration to a small volume around the prostate with goal to receive a better registration of the prostate than if full sized images are used. A registration program, utilizing NMI, was written and optimized in MatLab. Four Magnetic Resonance (MR) series and one Computed Tomographic (CT) series where taken over the pelvic area of five patients with the diagnosis prostate cancer. The prostate were delineated by a physician. By adding margin to the delineations five different sized Regions of Interest (ROI) where created.  The smallest ROI precisely covered the prostate while the largest covered the whole image. The deviation in Center of Mass (CoM) between the images and the Percentage Volume Overlap (PVO) were calculated and used as a measure of alignment. The registrations performed with sub-volumes showed an improvement compared to those that used full-volume while registering a MR image to another MR image. In one third of the cases a 2 cm margin to the prostate is preferable. A 3 cm margin is the most favorable option in another third of the cases. The use of sub-volumes to register MR images to CT series turned out to be unpredictable with poor accuracy. Full sized image registration between two MR image pairs has a high precision but, due to the motion of the prostate, poor accuracy. As a result of the high information content in the MR images both high precision as well as high accuracy can be achieved by the use of sub-volume registration. CT images do not contain the same amount of image information around the prostate and the sub-volume based registrations between MR and CT images are hence inconsistent with a low precision.  

The main objective of this thesis is the automatic modeling, understanding and segmentation of diffusively infiltrative tumors known as Diffuse Low-Grade Gliomas. Two approaches exploiting anatomical and spatial prior knowledge have been proposed. We first present the construction of a tumor specific probabilistic atlas describing the tumors' preferential locations in the brain. The proposed atlas constitutes an excellent tool for the study of the mechanisms behind the genesis of the tumors and provides strong spatial cues on where they are expected to appear. The latter characteristic is exploited in a Markov Random Field based segmentation method where the atlas guides the segmentation process as well as characterizes the tumor's preferential location. Second, we introduce a concurrent tumor segmentation and registration with missing correspondences method. The anatomical knowledge introduced by the registration process increases the segmentation quality, while progressively acknowledging the presence of the tumor ensures that the registration is not violated by the missing correspondences without the introduction of a bias. The method is designed as a hierarchical grid-based Markov Random Field model where the segmentation and registration parameters are estimated simultaneously on the grid's control point. The last contribution of this thesis is an uncertainty-driven adaptive sampling approach for such grid-based models in order to ensure precision and accuracy while maintaining robustness and computational efficiency. The potentials of both methods have been demonstrated on a large data-set of heterogeneous Diffuse Low-Grade Gliomas. The proposed methods go beyond the scope of the presented clinical context due to their strong modularity and could easily be adapted to other clinical or computer vision problems.