Positron Emission Tomography (PET) is a technology that uses short-lived radio nuclides altered by disease and precede changes that can be visualized by cross-sectional imaging. Over the last decade, this technique has become an important clinical tool for detection of tumors, follow-up treatment and drug research, providing an understanding of dynamic physiological processes. Since PET needs improved reconstruction algorithms to facilitate clinical diagnosis, we will investigate an improved iterative algorithm.
Amongst current algorithms applied for PET reconstruction, ART was first proposed as a method of reconstruction from CT projections. With appropriate tuning, the convergence of these algorithms could be very fast indeed. However, the quality of reconstruction using these methods has not been thoroughly investigated. We study a variant of these algorithms.
We present the state of the art, review well-known ART and investigate an optimum dynamically-changing block structure for the not yet fully explored variable-Block ART, which uses jointly the Inter-Update Metz filter for regularization and exploits the full symmetries in PET scanners. This reveals significant acceleration of initial convergence to an acceptable reconstruction of inconsistent cases. To assess the quality and analyze any discrepancy of the reconstructed images, two figures of merit (FOMs) are used to evaluate two 3D Data phantoms acquired on a GE-Advance scanner for high statistics.
A wide range of image processing studies are based on the extraction of texture features, the analysis of input data and the identification and design of appropriate classifiers given a particular application, for instance, in the fields of industrial inspection, remote sensing, medicine or biology amongst others. In this paper, we introduce a novel generalized classification framework for texture imagery based on a novel building blocks system architecture and present the advantages of such a system to tackle a variety of image analysis problems at the same time of obtaining good classification performances. Firstly, an overview of the system architecture is described from the texture feature extraction module to the data analysis and the classification building blocks. Thus, we obtain an optimized and generic classification framework which is highly flexible due to its scalable building blocks system approach and provides the facility to extend easily the study obtained for textural images to other kind of imagery. The results of this generalized classification framework are validated using imagery from two different application fields where texture plays a key role. The first one is in the field of remote sensing for agriculture crops classification and the second one, in the area of non-destructive industrial inspection.
Neural Networks and Fuzzy systems are considered two of the most important artificial intelligent algorithms which provide classification capabilities obtained through different learning schemas which capture knowledge and process it according to particular rule-based algorithms. These methods are especially suited to exploit the tolerance for uncertainty and vagueness in cognitive reasoning. By applying these methods with some relevant knowledge-based rules extracted using different data analysis tools, it is possible to obtain a robust classification performance for a wide range of applications. This paper will focus on non-destructive testing quality control systems, in particular, the study of metallic structures classification according to the corrosion time using a novel cellular neural network architecture, which will be explained in detail. Additionally, we will compare these results with the ones obtained using the Fuzzy C-means clustering algorithm and analyse both classifiers according to its classification capabilities.
An unavoidable problem of metal structures is their exposure to rust degradation during their operational life. Thus, the surfaces need to be assessed in order to avoid potential catastrophes. There is considerable interest in the use of patch repair strategies which minimize the project costs. However, to operate such strategies with confidence in the long useful life of the repair, it is essential that the condition of the existing coatings and the steel substrate can be accurately quantified and classified.
This paper describes the application of fuzzy set theory for steel surfaces classification according to the steel rust time. We propose a semi-automatic technique to obtain image clustering using the Fuzzy C-means (FCM) algorithm and we analyze two kinds of data to study the classification performance. Firstly, we investigate the use of raw images’ pixels without any pre-processing methods and neighborhood pixels. Secondly, we apply Gaussian noise to the images with different standard deviation to study the FCM method tolerance to Gaussian noise. The noisy images simulate the possible perturbations of the images due to the weather or rust deposits in the steel surfaces during typical on-site acquisition procedures
The exposure of metallic structures to rust degradation during their operational life is a known problem and it affects storage tanks, steel bridges, ships, etc. In order to prevent this degradation and the potential related catastrophes, the surfaces have to be assessed and the appropriate surface treatment and coating need to be applied according to the corrosion time of the steel. We previously investigated the potential of image processing techniques to tackle this problem. Several mathematical algorithms methods were analyzed and evaluated on a database of 500 images. In this paper, we extend our previous research and provide a further analysis of the textural mathematical methods for automatic rust time steel detection. Statistical descriptors are provided to evaluate the sensitivity of the results as well as the advantages and limitations of the different methods. Finally, a selector of the classifiers algorithms is introduced and the ratio between sensitivity of the results and time response (execution time) is analyzed to compromise good classification results (high sensitivity) and acceptable time response for the automation of the system.
Non-intrusive inspection and non-destructive testing of manufactured objects with complex internal structures typically requires the enhancement, analysis and visualization of high-resolution volumetric data. Given the increasing availability of fast 3D scanning technology (e.g. cone-beam CT), enabling on-line detection and accurate discrimination of components or sub-structures, the inherent complexity of classification algorithms inevitably leads to throughput bottlenecks. Indeed, whereas typical inspection throughput requirements range from 1 to 1000 volumes per hour, depending on density and resolution, current computational capability is one to two orders-of-magnitude less. Accordingly, speeding up classification algorithms requires both reduction of algorithm complexity and acceleration of computer performance. A shape-based classification algorithm, offering algorithm complexity reduction, by using ellipses as generic descriptors of solids-of-revolution, and supporting performance-scalability, by exploiting the inherent parallelism of volumetric data, is presented. A two-stage variant of the classical Hough transform is used for ellipse detection and correlation of the detected ellipses facilitates position-, scale- and orientation-invariant component classification. Performance-scalability is achieved cost-effectively by accelerating a PC host with one or more COTS (Commercial-Off-The-Shelf) PCI multiprocessor cards. Experimental results are reported to demonstrate the feasibility and cost-effectiveness of the data-parallel classification algorithm for on-line industrial inspection applications.
Moire methods are optical methods that are based on the effect of superposition of grating lines and have been widely used in the context of industrial applications for shape analysis, for non-contact measurements, and for quality control of industrial components. In applications the following computations: image filtering, fringe skeletonizing and fringe numbering have to be performed for each test object, before comparison between the numerically reconstructed test object shape and its CAD model. In order to reduce the computing time required by the preceding computations, the inverse moire technique has been introduced by Harthong. Instead of using a grating made of parallel straight lines, the inverse moire technique uses a pre-computed specific gratin, that is formed of curved lines such that the moire pattern is composed of parallel straight fringes if the test object shape is conformed to its CAD model. Defects are then characterized by a deformation and a curvature of these parallel fringes. In this paper, we present examples showing that standard fringe extraction by automatic thresholding is not that easy. To overcome this difficulty, we propose a four stage process algorithmical approach that allows fringe detection in inverse moire images with high sensitivity and specificity. First we used the well-known image processing technique called unsharp masking, to enhance moire image and to emphasize low contrasted fringes. The second step is to extract bright fringes by image segmentation and constrained contour modeling. After detection of these bright fringes inside the zone of interest of the moire image, we get the thick skeleton of adjacent background and of dark fringes. The third step is to skeletonize this thick skeleton of adjacent background and of dark fringes, using morphological thinning of well-composed sets, that assures that each fringe skeleton will be one pixel thick, at the difference of standard thinning techniques. The fourth step is to apply a graph technique to isolate the individual dark fringes. When all these four steps have been followed, one is left with a binary image showing the dark fringe pattern skeleton. The experimental results that have been obtained have shown the robustness of this algorithmical approach, for the analysis of noisy inverse moire images.