Biomedical Image Registration by means of Bacterial Foraging Paradigm

Image registration (IR) is the process of geometric overlaying or alignment f two or more 2D/3D images of the same scene (unimodal registration), taken r not at different time slots, from different angles, and/or by different image acquisition ystems (multimodal registration). Technically, image registration implies  complex optimization of different parameters, performed at local or/and global evel. Local optimization methods often fail because functions of the involved metrics ith respect to transformation parameters are generally nonconvex and irregular, and lobal methods are required, at least at the beginning of the procedure. This paper resents a new evolutionary and bio-inspired robust approach for IR, Bacterial Foraging ptimization Algorithm (BFOA), which is adapted for PET-CT multimodal nd magnetic resonance image rigid registration. Results of optimizing the normalized utual information and normalized cross correlation similarity metrics validated he efficacy and precision of the proposed method by using a freely available medical mage database

Accuracy of activity quantitation of F-18 fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) imaging using simulated malignant tumors

This thesis involves a procedure, which calculated and compared the sum of all the pixel counts, threshold pixel counts sum of a 3D PET image and mean and maximum pixel count of one single transaxial slice (2D) of simulated tumors for a chosen region of interest (ROI). A calibration factor was multiplied by the sum of the pixel counts, threshold pixel counts sum of all the transaxial slices, and the mean, and maximum pixel counts of one single transaxial slice in an ROI to calculate for the activity of the tumor. This activity calculated was compared with the real activity values. The results showed that the sum of all the pixel counts with applied threshold is better to calculate the activity of tumor with greater accuracy. These findings suggest that a 3D distribution of sum of all the pixel counts was able to calculate the activity of malignant tumors and lung lesions with better accuracy

Dynamic Multivariate Simplex Splines For Volume Representation And Modeling

Volume representation and modeling of heterogeneous objects acquired from real world are very challenging research tasks and playing fundamental roles in many potential applications, e.g., volume reconstruction, volume simulation and volume registration. In order to accurately and efficiently represent and model the real-world objects, this dissertation proposes an integrated computational framework based on dynamic multivariate simplex splines (DMSS) that can greatly improve the accuracy and efficacy of modeling and simulation of heterogenous objects. The framework can not only reconstruct with high accuracy geometric, material, and other quantities associated with heterogeneous real-world models, but also simulate the complicated dynamics precisely by tightly coupling these physical properties into simulation. The integration of geometric modeling and material modeling is the key to the success of representation and modeling of real-world objects. The proposed framework has been successfully applied to multiple research areas, such as volume reconstruction and visualization, nonrigid volume registration, and physically based modeling and simulation

Robust similarity metrics for the registration of 3D multimodal medical images

In this paper, we develop data driven registration algorithms, relying on pixel similarity metrics, that enable an accurate rigid registration of dissimilar single or multimodal 2D/3D medical images . Gross dissimilarities are handled by considering similarity measures related to robust M-estimators . Fast stochastic multigrid optimization algorithms are used to minimize these similarity metrics . The proposed robust similarity metrics are compared to the most popular standard similarity metrics on real MRI/MRI and MRI/SPECT image pairs showing gross dissimilarities . A blinded evaluation of the algorithm was performed, using as gold standard a prospective, marker-based registration method, by participating in a registration evaluation project (Vanderbilt University) . Our robust similarity measures compare favourably with all standard (non robust) techniques .Le recalage non supervisé d'images médicales volumiques reste un problème difficile en raison de l'importante variabilité et des grandes différences d'information pouvant apparaître dans des séquences d'images de même modalité ou dans des couples d'images multimodales. Nous présentons dans cet article des méthodes robustes de recalage rigide d'images 2D et 3D monomodales et multimodales, reposant sur la minimisation de mesures de similarité inter-images. Les méthodes proposées s'appuient sur la théorie de l'estimation robuste et mettent en oeuvre des M-estimateurs associés à des techniques d'optimisation stochastique multigrilles rapides. Ces estimateurs robustes sont évalués à travers le recalage d'images médicales volumiques monomodales (IRM/IRM) et multimodales (IRM/TEMP). Ils sont comparés aux autres fonctions de similarité classiques, proposées dans la littérature. Les méthodes de recalage robustes ont, en particulier, été validées dans le cadre d'un protocole comparatif mis en place par l'Université de Vanderbilt. Elles sont actuellement utilisées en routine clinique et conduisent, tant pour les images de même modalité que pour les images multimodales à une précision sous-voxel, comparable aux meilleures méthodes actuelles. Elles permettent de plus de recaler des couples d'images sur lesquels les méthodes classiques échouent

Development and characterization of methodology and technology for the alignment of fMRI time series

This dissertation has developed, implemented and tested a novel computer based system (AUTOALIGN) that incorporates an algorithm for the alignment of functional Magnetic Resonance Image (fMRI) time series. The algorithm assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lie on the directions correspondent to two of the eigenvectors of inertia of the volume, at the intersections with the head boundary. The eigenvectors are found weighting the inertia components with the voxel\u27s intensity values assumed as mass. The three reference points are found in the same position, relative to the origin of the head coordinate system, in both test and reference brain images. Intensity correction is performed at sub-voxel accuracy by tri-linear interpolation. A test fMR brain volume in which controlled simulations of rigid-body transformations have been introduced has preliminarily assessed system performance. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations have been retrieved automatically and the values of the motion parameters compared to those obtained by the Statistical Parametric Mapping (SPM99), and the Automatic Image Registration (AIR 3.08). Results indicated that AUTOALIGN offers subvoxel accuracy in correcting both misalignment and intensity among time points in fMR images time series, and also that its performance is comparable to that of SPM99 and AIR3.08

Sub-pixel Registration In Computational Imaging And Applications To Enhancement Of Maxillofacial Ct Data

In computational imaging, data acquired by sampling the same scene or object at different times or from different orientations result in images in different coordinate systems. Registration is a crucial step in order to be able to compare, integrate and fuse the data obtained from different measurements. Tomography is the method of imaging a single plane or slice of an object. A Computed Tomography (CT) scan, also known as a CAT scan (Computed Axial Tomography scan), is a Helical Tomography, which traditionally produces a 2D image of the structures in a thin section of the body. It uses X-ray, which is ionizing radiation. Although the actual dose is typically low, repeated scans should be limited. In dentistry, implant dentistry in specific, there is a need for 3D visualization of internal anatomy. The internal visualization is mainly based on CT scanning technologies. The most important technological advancement which dramatically enhanced the clinician\u27s ability to diagnose, treat, and plan dental implants has been the CT scan. Advanced 3D modeling and visualization techniques permit highly refined and accurate assessment of the CT scan data. However, in addition to imperfections of the instrument and the imaging process, it is not uncommon to encounter other unwanted artifacts in the form of bright regions, flares and erroneous pixels due to dental bridges, metal braces, etc. Currently, removing and cleaning up the data from acquisition backscattering imperfections and unwanted artifacts is performed manually, which is as good as the experience level of the technician. On the other hand the process is error prone, since the editing process needs to be performed image by image. We address some of these issues by proposing novel registration methods and using stonecast models of patient\u27s dental imprint as reference ground truth data. Stone-cast models were originally used by dentists to make complete or partial dentures. The CT scan of such stone-cast models can be used to automatically guide the cleaning of patients\u27 CT scans from defects or unwanted artifacts, and also as an automatic segmentation system for the outliers of the CT scan data without use of stone-cast models. Segmented data is subsequently used to clean the data from artifacts using a new proposed 3D inpainting approach

Assessment of 3D movements in the lumbar and cervical spine with a new CT based method

Background: Numerous methods for measuring segmental motion in spine have been described. However, because of the inaccessibility of the spine and the complexity of segmental movements, most of the noninvasive methods in use today have low accuracy or are unable to detect movements in all three cardinal axes. Almost all in vivo methods used for analysing segmental motion are based on twodimensional (2D) radiographic examinations. Radiostereometris Analysis is so far the most accurate method to detect three-dimensional (3D) motion. Specific aim: To develop and evaluate a non-invasive method for motion analysis of the spine using computed tomography (CT). Methods: We studied segmental motion in a custom-made spine model, healthy subjects, and a small series of patients operated with total disc replacement. The subjects and patients were examined in flexion and extension on a fourth generation spiral CT unit. Analyses of the segmental movements in lumbar and cervical spine were done with a in-house developed software tool. Results: In the lumbar spine the accuracy was 0.6 mm for translation and 1 degree for rotation in the model study. Movements of more than 1 mm could be visual detected. The repeatability on healthy subjects was 2.8 degrees in rotation and 1.8 mm in translation in vertebral segment. The mean facet joint 3D movement was for the right 6.1 mm and for the left 6.9 mm in L4-L5 segment and for the L5-S1 segment for the right facet 4.5 mm and 4.8 mm for the left. Mean rotation in the sagittal plane was 14.3 degrees in L4-L5 and 10.2 degrees in L5-S1. In patients with total disc replacement the mean rotation in the sagittal plane at the operated level (L5-S1) was 5.4 degrees before surgery and 6.8 after surgery. In the adjacent level (L4-L5) the mean rotation (degrees) was 7.7 before and 9.2 after surgery. The 3D translation in the operated level the left facet was 3.6 mm before and 4.5 mm after surgery and for the right facet joint 3.4 mm before to 3.6 mm after surgery. In the cervical spine the accuracy was 0.7 degrees in rotation and 0.5 mm in translation in the model study. The repeatability on the model was 1.1 degrees in rotation and 0.3 mm in translation. The repeatability on patients was 2.3 degrees in rotation and 1.4 mm in translation. The median movement for the patient was in the sagittal plane for rotation 6.28 and translation 0.1mm, coronal plane 1.68 and 0.6 mm, and for the transverse plane 1.38 and 0.6 mm in translation Conclusion: We have developed a non-invasive CT based method to study the 3D segmental movement in the spine. It has been tested in a model study, on healthy subjects and on patients with total disc replacement in cervical and lumbar spine. We believe that this method for detecting movements in the spine is useful both in research and for clinical use

Three-dimensional morphanalysis of the face.

The aim of the work reported in this thesis was to determine the extent to which orthogonal two-dimensional morphanalytic (universally relatable) craniofacial imaging methods can be extended into the realm of computer-based three-dimensional imaging. New methods are presented for capturing universally relatable laser-video surface data, for inter-relating facial surface scans and for constructing probabilistic facial averages. Universally relatable surface scans are captured using the fixed relations principle com- bined with a new laser-video scanner calibration method. Inter- subject comparison of facial surface scans is achieved using inter- active feature labelling and warping methods. These methods have been extended to groups of subjects to allow the construction of three-dimensional probabilistic facial averages. The potential of universally relatable facial surface data for applications such as growth studies and patient assessment is demonstrated. In addition, new methods for scattered data interpolation, for controlling overlap in image warping and a fast, high-resolution method for simulating craniofacial surgery are described. The results demonstrate that it is not only possible to extend universally relatable imaging into three dimensions, but that the extension also enhances the established methods, providing a wide range of new applications