Convergence of the simultaneous algebraic reconstruction technique (SART)
In 1984, the simultaneous algebraic reconstruction technique (SART) was developed as a major refinement of the algebraic construction technique (ART). Since then, the SART approach remains a powerful tool for iterative image reconstruction. However, the convergence of the SART has never been established. This long-standing conjecture is proven under the condition that coefficients of the linear imaging system are non-negative. It is shown that from any initial value the sequence generated by the SART converges to a weighted least square solution. The importance of the SART and several relevant issues are also discussed.

title: Blind deblurring of spiral CT images
abstract: To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.

title: An Optimized Two-Step Solution for Updating XML Views
abstract: View updating is a long standing difficult problem. Given a view defined over base data sources and a view update, there are several different updates over the base data sources, called translations, that perform the update. A translation is said to be correct if it performs the update and at the same time does not update any portion of the view not specified in the update (no view side-effects). The view update problem finds a correct translation for a given view update if one exists. In the relational scenario, previous research has attempted to study the view update problem either by utilizing only the schema knowledge, or by directly examining the base data. While utilizing only the schema knowledge is very efficient, we are not guaranteed to find a correct translation even if one exists. On the other hand, examining the base data is guaranteed to find a correct translation if one exists, but is very time-consuming. The view update problem is even more complex in the XML context due to the nested hierarchical structure of XML and the restructuring capabilities of the XQUERY view specification. In this paper we propose a schema-centric framework, named HUX, for efficiently updating XML views specified over relational databases. HUX is complete (always finds a correct translation if one exists) and is efficient. The efficiency of HUX is achieved as follows. Given a view update, HUX first exploits the schema to determine whether there will never be a correct translation, or there will always be a correct translation. Only if the update cannot be classified using the schema, HUX will examine the base data to determine if there is a correct translation. This data-level checking is further optimized in HUX, by exploiting the schema knowledge extracted in the first step to significantly prune the space of potential translations that is explored. Experiments illustrate the performance benefits of HUX over previous solutions.

title: Nonlinear behavior of the parameterized Logistic differential systems
abstract: This paper deals with local stability, Hopf bifurcation and chaos in the parameterized Logistic differential systems with delay. By applying the Halanay inequality, the local stability of the Logistic differential systems is discussed. The stability of bifurcation periodic solutions and the direction of Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. In the end, interesting nonlinear behavior of the parameterized Logistic differential systems with a single parameter delay is detected by numerical examples.

title: Improved Results on Solving Quadratic Programming Problems with Delayed Neural Network
abstract: In this paper, in terms of a linear matrix inequality (LMI), using a delayed Lagrangian network to solve quadratic programming problems, sufficient conditions on delay-dependent and delay-independent are given to guarantee the globally exponential stability of the delayed neural network at the optimal solution. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper improved the ones reported in the existing literatures and the proposed sufficient condition can be checked easily by solving LMI. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.

title: Quantitative steganalysis of binary images
abstract: We propose a quantitative steganalysis method to detect hidden information embedded by flipping pixels along boundaries in binary images. We model steganographic embedding as an additive noise process and use compression rate as a distinguishing statistic that aids in discriminating between stego-images and cover-images. We specifically use the JBIG 2 binary image compression algorithm to derive a quantitative relation between compression rate and embedding rate. Based on this relationship, a practical steganalysis technique is proposed by examining the change of compression rate as embedding rate increases. Experiments conducted show that the proposed technique can reliably detect a steganographic embedding process that flips boundary pixels. Furthermore, it can estimate embedding rate with reasonable accuracy.

title: Automated Extraction of Microtubules and Their Plus-Ends
abstract: Though electron tomography opens up new possibilities in imaging the microtubule and the fine plus-end structures, the interpretation of the acquired data remains an obstacle due to the low SNR and the cluttered cellular environment. The automatic extraction of plus-end is especially challenging since they have complex and varying conformations beyond the capacity of existing segmentation methods. We propose an automated approach to extracting the microtubule plus-end with a coarse to fine scale scheme consisting of volume enhancement and plus-end segmentation. To make the segmentation robust against confusing image features, we have fully incorporated the prior knowledge of microtubules and plus-ends into our model-based framework. Experimental results demonstrate that our automated method produces results comparable to the manual segmentation but using only a fraction of the manual segmentation time. The automated approach also segments more fine structures that could be overlooked by human operators.