Image Transformation Algorithm Research Articles

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4 °. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.

Image-Based Estimation of Ventricular Fiber Orientations for Personalized Modeling of Cardiac Electrophysiology

Technological limitations pose a major challenge to acquisition of myocardial fiber orientations for patient-specific modeling of cardiac (dys)function and assessment of therapy. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4°. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.

Integrated Sensing for Entry, Descent, and Landing of a Robotic Spacecraft

We present an integrated sensing approach for enabling autonomous landing of a robotic spacecraft on a hazardous terrain surface; this approach is active during the spacecraft descent profile. The methodology incorporates an image transformation algorithm to interpret temporal imagery land data, perform real-time detection and avoidance of terrain hazards that may impede safe landing, and increase the accuracy of landing at a desired site of interest using landmark localization techniques. By integrating a linguistic rule-based engine with linear algebra and computer vision techniques, the approach suitably addresses inherent uncertainty in the hazard assessment process while ensuring computational simplicity for real-time implementation during spacecraft descent. The proposed approach is able to identify new hazards as they emerge and also remember the locations of past hazards that might impede spacecraft landing. We provide details of the methodology in this paper and present simulation results of the approach applied to a representative Mars landing descent profile.

Selection of image transformations in the computer analysis of cytological specimens

The paper considers the problem of the diagnostic analysis of blood system tumors (hemoblastosis) using special techniques. Images of lymph node specimen are taken from three groups of patients with such diagnoses as indolent chronic lymphatic leukemia, transformed chronic lymphatic leukemia, and de novo large and mixed cell lymphomas serving as source data. The analysis of the feature description showed that different sets of informative features correspond to different diagnoses. Thus, special techniques of image analysis and recognition, which allow taking into account the informative nature of images when selecting image transformation algorithms are required. Such possibilities are offered by the technique of image transformation selection when solving recognition problems. This technique allows taking into account the description peculiarities of each class of images and utilizing the recognition algorithm appropriate for the given class. The paper shows that application of this technique helps substantially improve the accuracy of the recognition.

Development of a phase-displacement equation for panoramic interferometry

We developed the parametric equations that are needed to quantify the modulations in the sensitivity vector that occur when the phase-displacement equation is applied to make panoramic interferometric measurements. The measurement system relies on two collinear panoramic annular lenses, one to illuminate and the other to image their surroundings. When a coherent light source is used and a reference beam is added, interference occurs over the region of interest defined by the illuminating and viewing lenses. A holographic system is used to demonstrate the approach and quantify the analysis. We obtained interference fringes in real time by comparing holograms recorded before and after a section of cylindrical pipe is displaced relative to the measurement system. The annular images and the holographic fringes are acquired and stored digitally in a computer system, and image transformation algorithms are applied to remove optical distortions in the holographic patterns. Excellent agreement is obtained when the fringe loci are compared with those predicted on the basis of theory.

Transformation of gray level and color images

Image transformations play a major role in image processing, pattern recognition, and computer vision. One of the applications of image transformation is to convert an input image into another one, which can be processed more easily and/or with a better result. Another application is to generate various new sample images from an existing image. Most of the papers dealing with image transformation describe algorithms that can only process binary images, and therefore have limited applications. In this paper, new image transformation algorithms capable of handling gray level and color images are proposed. These algorithms perform the mapping and filling at the same time, while preserving the connectivity of the original image. Also, they keep the continuity of the intensities by assigning the proper value of the neighbors to adequately fill the empty spots. The proposed algorithms can deal with all kinds of images, and are applicable to producing a large number of gray level and color images in a short time, hence, they will be very useful for image processing and pattern recognition.

Parallel Evaluation of Hierarchical Image Databases

This paper presents a hierarchical image transformation and efficient parallel algorithms for its evaluation. This transformation maps image structures onto code trees of different height, depending on the size of the structure. Thereby, important structures are effectively separated from the background. The inherent parallelism of such a hierarchical image transformation is outlined. The algorithms are domain independent and were successfully used for workpiece recognition and for traffic sign detection. A communication module for farmer-worker applications that supports specialized processors, like frame grabbers or display units, as well as the parallel recognition process is illustrated in detail. The implementation is done on a 9-node transputer image processing system. The functionality from grabbing an image and low-level filtering to transformation and high-level symbolic pattern analysis is integrated. Runtimes for the analysis of traffic scenes of less than half a second were achieved. Illustrated examples and experimental results are discussed.