The maximum likelihood (ML) approach to estimating the radioactive distribution in the body cross section has become very popular among researchers in emission computed tomography (ECT) since it has been shown to provide very good images compared to those produced with the conventional filtered backprojection (FBP) algorithm. The expectation maximization (EM) algorithm is an often-used iterative approach for maximizing the Poisson likelihood in ECT because of its attractive theoretical and practical properties. Its major disadvantage is that, due to its slow rate of convergence, a large amount of computation is often required to achieve an acceptable image. Here, the authors present a row-action maximum likelihood algorithm (RAMLA) as an alternative to the EM algorithm for maximizing the Poisson likelihood in ECT. The authors deduce the convergence properties of this algorithm and demonstrate by way of computer simulations that the early iterates of RAMLA increase the Poisson likelihood in ECT at an order of magnitude faster that the standard EM algorithm. Specifically, the authors show that, from the point of view of measuring total radionuclide uptake in simulated brain phantoms, iterations 1, 2, 3, and 4 of RAMLA perform at least as well as iterations 45, 60, 70, and 80, respectively, of EM. Moreover, the authors show that iterations 1, 2, 3, and 4 of RAMLA achieve comparable likelihood values as iterations 45, 60, 70, and 80, respectively, of EM. The authors also present a modified version of a recent fast ordered subsets EM (OS-EM) algorithm and show that RAMLA is a special case of this modified OS-EM. Furthermore, the authors show that their modification converges to a ML solution whereas the standard OS-EM does not.

A row-action alternative to the EM algorithm for maximizing likelihood in emission tomography

A locally adaptive thresholding algorithm, concerning the extraction of targets from a given field of background, is proposed. Conventional histogram-based or global-type methods are deficient in detecting small targets of possibly low contrast as well. The present research is notable for solving the mentioned problems by introducing (1) shape connectivity measure based on co-occurrence statistics for threshold evaluation; and (2) no-target identification procedure for modeling a local-processing paradigm. In this manner, thresholds are determined adaptively even in the presence of space-varying noise or clutter. Experiments show that the results are reliable and even outperform those that manual operations can achieve for global thresholding. >

Automatic target segmentation by locally adaptive image thresholding

A general systematic method for the detection and segmentation of bright targets is developed. We use the term "bright target" to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.

/pdf/segmentation-of-bright-targets-using-wavelets-and-adaptive-4mygwp0zfp.pdf

Segmentation of bright targets using wavelets and adaptive thresholding

Tomographic methods are used for the investigation of three-dimensional compressible flow fields by means of interferometric methods. A modified algebraic reconstruction technique algorithm is applied. The algorithm proved to give reliable reconstructions from experimentally measured projection data in the case of an unrestricted angular view. The method was used for the reconstruction of density distributions of weakly perturbed supersonic free jets exiting from a deformed Laval nozzle. Even small perturbations of the jet resulted in significant three-dimensional effects. Reconstruction of a multiple system of jets emanating from a sievelike nozzle showed mutual interactions between the constituent jets. For the investigation of unsteady flows a setup for the recording of holographic interferograms was designed. Here, because of experimental restrictions, only a limited angular range of views was accessible. In the context of this limited-view geometry, reconstructions revealed considerable distortions for objects containing steep gradients.

Interferometric tomography for flow visualization of density fields in supersonic jets and convective flow.

Multiplicative iterative methods in computed tomography

Some new multiplicative algorithms for image reconstruction from projections

Real-time image segmentation is a critical part of an automatic target recognizer (ATR). The segmentation of poorly resolved targets in low contrast thermal video images is a challenging task. Most edge-based segmenters are too susceptible to noise, contrast variation, and target boundary discontinuity. The problem is the lack of a fast (video rate) and robust method of grouping the relevant edge elements together while rejecting the irrelevant ones. We have overcome this problem by combining the processing power of a single instruction multiple data (SIMD) computer with a newly developed model-directed segmentation algorithm.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Parallel algorithm for model-directed real-time image segmentation

Although there are many edge detection operators that are used in Automatic Target Recognition (ATR) Systems we believe that the required performance can be accomplished using simple operators such as the Sobel operator with minor modification through pre and postprocessing. In this paper we describe two methods that enhance the Sobel performance. The first which can be considered pre processing increases the effective size of the operator to 5*5 The second which is postprocessing modifies the edge magnitude output based on the consistency of the local edge direction. Both methods can be easily implemented on SIMD machines and they are effective in deleting isolated edge points which usually are not part of interesting targets. We will describe the implementation of these techniques on a SIMD machine and study their effect on the performance of an ATR system. 1.

Use of directional consistency with Sobel edge detector

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Some new multiplicative algorithms for image reconstruction from projections

Parallel algorithm for model-directed real-time image segmentation

Use of directional consistency with Sobel edge detector