Medical imaging plays an increasingly important role in modern healthcare. In medical imaging, it is often relevant to relate different images to each other, something which can prove challenging, since there rarely exists a pre-defined mapping between the pixels in different images. Hence, there is a need to find such a mapping/transformation, a procedure known as image registration. Over the years, image registration has been proved useful in a number of clinical situations. Despite this, current use of image registration in clinical practice is rather limited, typically only used for image fusion. The limited use is, to a large extent, caused by excessive computation times, lack of established validation methods/metrics and a general skepticism toward the trustworthiness of the estimated transformations in deformable image registration. This thesis aims to overcome some of the issues limiting the use of image registration, by proposing a set of technical contributions and two clinical applications targeted at improved clinical efficiency. The contributions are made in the context of a generic framework for non-parametric image registration and using an image registration method known as the Morphon.  In image registration, regularization of the estimated transformation forms an integral part in controlling the registration process, and in this thesis, two regularizers are proposed and their applicability demonstrated. Although the regularizers are similar in that they rely on local structure analysis, they differ in regard to implementation, where one is implemented as applying a set of filter kernels, and where the other is implemented as solving a global optimization problem. Furthermore, it is proposed to use a set of quadrature filters with parallel scales when estimating the phase-difference, driving the registration. A proposal that brings both accuracy and robustness to the registration process, as shown on a set of challenging image sequences. Computational complexity, in general, is addressed by porting the employed Morphon algorithm to the GPU, by which a performance improvement of 38-44x is achieved, when compared to a single-threaded CPU implementation. The suggested clinical applications are based upon the concept paint on priors, which was formulated in conjunction with the initial presentation of the Morphon, and which denotes the notion of assigning a model a set of properties (local operators), guiding the registration process. In this thesis, this is taken one step further, in which properties of a model are assigned to the patient data after completed registration. Based upon this, an application using the concept of anatomical transfer functions is presented, in which different organs can be visualized with separate transfer functions. This has been implemented for both 2D slice visualization and 3D volume rendering. A second application is proposed, in which landmarks, relevant for determining various measures describing the anatomy, are transferred to the patient data. In particular, this is applied to idiopathic scoliosis and used to obtain various measures relevant for assessing spinal deformity. In addition, a data analysis scheme is proposed, useful for quantifying the linear dependence between the different measures used to describe spinal deformities.

Modern medicine has become reliant on medical imaging. Multiple modalities, e. g. magnetic resonance imaging (MRI), computed tomography (CT), etc. , are used to provide as much information about the patient as possible. The problem of geometrically aligning the resulting images is called image registration. Mutual information, an information theoretic similarity measure, allows for automated intermodal image registration algorithms. <br /><br /> In applications such as cancer therapy, diagnosticians are more concerned with the alignment of images over a region of interest such as a cancerous lesion, than over an entire image set. Attempts to register only the regions of interest, defined manually by diagnosticians, fail due to inaccurate mutual information estimation over the region of overlap of these small regions. <br /><br /> This thesis examines the region of union as an alternative to the region of overlap. We demonstrate that the region of union improves the accuracy and reliability of mutual information estimation over small regions. <br /><br /> We also present two new mutual information based similarity measures which allow for localized image registration by combining local and global image information. The new similarity measures are based on convex combinations of the information contained in the regions of interest and the information contained in the global images. <br /><br /> Preliminary results indicate that the proposed similarity measures are capable of localizing image registration. Experiments using medical images from computer tomography and positron emission tomography demonstrate the initial success of these measures. <br /><br /> Finally, in other applications, auto-detection of regions of interest may prove useful and would allow for fully automated localized image registration. We examine methods to automatically detect potential regions of interest based on local activity level and present some encouraging results.

Image registration is widely used in different areas nowadays. Usually, the efficiency, accuracy, and robustness in the registration process are concerned in applications. This thesis studies these issues by presenting an efficient intensity-based mono-modality rigid 2D-3D image registration method and constructing a novel mathematical model for intensity-based multi-modality rigid image registration. For mono-modality image registration, an algorithm is developed using RapidMind Multi-core Development Platform (RapidMind) to exploit the highly parallel multi-core architecture of graphics processing units (GPUs). A parallel ray casting algorithm is used to generate the digitally reconstructed radiographs (DRRs) to efficiently reduce the complexity of DRR construction. The optimization problem in the registration process is solved by the Gauss-Newton method. To fully exploit the multi-core parallelism, almost the entire registration process is implemented in parallel by RapidMind on GPUs. The implementation of the major computation steps is discussed. Numerical results are presented to demonstrate the efficiency of the new method. For multi-modality image registration, a new model for computing mutual information functions is devised in order to remove the artifacts in the functions and in turn smooth the functions so that optimization methods can converge to the optimal solutions accurately and efficiently. With the motivation originating from the objective to harmonize the discrepancy between the image presentation and the mutual information definition in previous models, the new model computes the mutual information function using both the continuous image function representation and the mutual information definition for continuous random variables. Its implementation and complexity are discussed and compared with other models. The mutual information computed using the new model appears quite smooth compared with the functions computed by others. Numerical experiments demonstrate the accuracy and efficiency of optimization methods in the case that the new model is used. Furthermore, the robustness of the new model is also verified.

General video stabilization techniques which are digital, mechanical and optical are discussed. Under the concept of video stabilization, various digital motion estimation and motion correction algorithms are implemented. For motion estimation, in addition to digital approach, a mechanical approach is implemented also. Then all implemented motion estimation and motion correction algorithms are compared with respect to their computational times and accuracies over various videos. For small amount of jitter, digital motion estimation performs well in real time. But for big amount of motion, digital motion estimation takes very long time so for these cases mechanical motion estimation is preferred due to its speed in estimation although digital motion estimation performs better. Thus, when mechanical motion estimation is used first and then this estimate is used as the initial estimate for digital motion estimation, the same accuracy as digital estimation is obtained in approximately the same time as mechanical estimation. For motion correction Kalman and Fuzzy filtering perform better than lowpass and moving average filtering.

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.

In this thesis, we present an algorithm for vision based detection of moving objects observed by IR sensors on a moving platform. In addition we analyze the performance of different approaches in each step of the algorithm. The proposed algorithm is composed of preprocessing, feature detection, feature matching, homography estimation and difference image analysis steps. First, a global motion estimation based on planar homography model is performed in order to compensate the motion of the sensor and moving platform where the sensors are located. Then, moving objects are identified on difference images of consecutive video frames with global motion suppression. Performance of the proposed algorithm is shown on different IR image sequences.

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.

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.  

Prostate cancer is a worldwide health concern for men. Needle biopsy is the most definitive form of cancer diagnosis. Target-specific biopsies can be performed under magnetic resonance imaging (MRI) guidance. However, needle placements are often inaccurate due to intra-operative prostate motion and the lack of motion compensation techniques. As a result, malignant tumors can be missed, which in turn will lead to an increased number of repeated biopsies and delaying of treatment. To increase the needle targeting accuracy, intra-operative prostate motion and deformation need to be studied so that motion compensation techniques can be developed accordingly. This thesis intends to make three main contributions: 1. A comprehensive survey of the state-of-art in image-guided prostate needle placement interventions. 2. Retrospective clinical accuracy validation of a MRI-guided robotic prostate biopsy system that was used in the U.S. National Cancer Institute for over 6 years. A 3D-3D registration algorithm consists of an initial two-step rigid alignment followed by a B-spline deformable transform was developed to align the pre- and post-needle insertion images. A total of 90 biopsies from 24 patients were studied. The mean target displacement, needle placement error, and clinical biopsy error were 5.2, 2.5, and 4.3 mm, respectively. 3. Development of a multi-slice-to-volume registration for intra-operative target localization. The algorithm aligns the planning volume with three orthogonal image slices of the prostate acquired immediately before needle insertion. It consists of a rigid registration followed by a deformable step using only the prostate region. The algorithm was validated on 14 clinical images sets from Brigham and Women's Hospital in Boston, Massachusetts. All registration errors were well below the radius of a clinically significant tumour (5 mm), and are considered clinically acceptable. The results show that there was a substantial amount of biopsy error caused by prostate motion and deformation during MRI-guided biopsy. This error can be reduced by using quantitative imaging techniques for prostate registration and motion compensation. In particular, the multi-slice-to-volume registration algorithm demonstrated the feasibility of intra-operative target localization and motion compensation; which in turn may improve the quality of MRI-guided prostate interventions. / Thesis (Ph.D, Computing) -- Queen's University, 2014-03-01 11:45:55.8

Modern image-guided surgery aids minimally-invasive, high-precision procedures that increase efficacy of treatment. This thesis investigates two research aims to improve precision and integration of intraoperative cone-beam CT (CBCT) imaging in guidance of head and neck (H&N) surgery. First, marker configurations were examined to identify arrangements that minimize target registration error (TRE). Best arrangements minimized the distance between the configuration centroid and surgical target while maximizing marker separation. Configurations of few markers could minimized TRE with more markers providing improved uniformity. Second, an algorithm for automatic registration of image and world reference frames was pursued to streamline integration of CBCT with real-time tracking and provide automatic updates per scan. Markers visible to the tracking and imaging systems are automatically co-localized and registered with equivalent accuracy and superior reproducibility compared to conventional registration. Such work helps the implementation of CBCT in H&N surgery to maximize surgical precision and exploit intraoperative image guidance.