Enhancement of microtubules in EM tomography
The interpretation of the EM tomography of microtubules is challenging due to the low SNR and low contrast of the volume data. Therefore, image enhancement is crucial for the subsequent segmentation and structural analysis of microtubules. In this paper, we propose a model based 3D image enhancement approach by combining transform domain technique and spatial domain techniques in three consecutive steps. The enhancement starts with an anisotropic invariant wavelet transform to effectively enhance the elongated features, followed by a 3D shape filter via eigen analysis to capture the local geometric properties of the tubular structure. The enhancement ends with a coherence enhancing diffusion to complete the interruptions along the microtubules. The contribution of this work is that we have tailored and improved each of the above techniques to exploit the unique geometric and photometric properties of microtubules. Experimental results indicate that our proposed approach has excellent performance in noise removal and enhancement of the tomography volume.

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.

title: Automated extraction of fine features of kinetochore microtubules and plus-ends from electron tomography volume
abstract: Kinetochore microtubules (KMTs) and the associated plus-ends have been areas of intense investigation in both cell biology and molecular medicine. Though electron tomography opens up new possibilities in understanding their function by imaging their high-resolution structures, the interpretation of the acquired data remains an obstacle because of the complex and cluttered cellular environment. As a result, practical segmentation of the electron tomography data has been dominated by manual operation, which is time consuming and subjective. In this paper, we propose a model-based automated approach to extracting KMTs and the associated plus-ends with a coarse-to-fine scale scheme consisting of volume preprocessing, microtubule segmentation and plus-end tracing. In volume preprocessing, we first apply an anisotropic invariant wavelet transform and a tube-enhancing filter to enhance the microtubules at coarse level for localization. This is followed with a surface-enhancing filter to accentuate the fine microtubule boundary features. The microtubule body is then segmented using a modified active shape model method. Starting from the segmented microtubule body, the plus-ends are extracted with a probabilistic tracing method improved with rectangular window based feature detection and the integration of multiple cues. Experimental results demonstrate that our automated method produces results comparable to manual segmentation but using only a fraction of the manual segmentation time.

title: A Generalized LMI-Based Approach to the Global Exponential Stability of Recurrent Neural Networks with Delay
abstract: A new theoretical result on the global exponential stability of recurrent neural networks with delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality (LMI), is a generalization and improvement over some previous criteria. A example is given to illustrate our results.

title: Stochastic Lotka-Volterra Competitive Systems with Variable Delay
abstract: In this paper we reveal that the environmental noise will not only suppress a potential population explosion in the stochastic Lotka-Volterra competitive systems with variable delay, but also make the solutions to be stochastically ultimately bounded. To reveal these interesting facts, we stochastically perturb the Lotka-Volterra competitive systems with variable delay ẋ(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))] into the Ito form dx(t)=diag(x1(t),...,xn(t))[b+Ax(t−δ(t))]dt+σx(t)dw(t) and show that although the solution to the original delay systems may explode to infinity in a finite time, with probability one that of the associated stochastic delay systems do not. We also show that the stochastic systems will be stochastically ultimately bounded without any additional conditions on the matrix A.

title: Steganalysis of boundary-based steganography using autoregressive model of digital boundaries
abstract: In this paper, we present a novel technique for the steganalysis of digital documents, when the secret message is embedded along the boundaries of text characters or other symbols in the documents. The proposed technique uses an auto-regressive model to detect marked documents, as well as to estimate the relative length of the embedded messages. Experimental results demonstrate the effectiveness and accuracy of the proposed technique

title: Robust Document Image Authentication
abstract: In this paper, we propose a novel method for robust document image authentication. In the proposed method, characters and symbols on a binary document are first grouped into different classes based on k-means clustering in the feature space. Labels are then assigned to the different classes. An ordered sequence of labels formed from the characters and symbols on the document is then used to compute a digital signature. This is achieved by using a cryptographic hash function and a secret key. The computed signature can be appended at the end of an electronic document file, or printed on a hardcopy document in the form of a bar code. Using this method, we will be able to detect any intentional content alteration to the document, but at the same time tolerate moderate amount of noise introduced by printing, scanning, and photocopying of the document. Experimental results demonstrate the effectiveness of the proposed approach.

title: Global Exponential Stability and Periodicity of CNNs with Time-Varying Discrete and Distributed Delays
abstract: In this paper, based on the Lyapunov stability analysis for functional differential equations, the problems of global exponential stability and periodic solutions for a class of cellular neural networks with time-varying delays and distributed delays are investigated. By constructing a new suitable Lyapunov functional, some simple sufficient conditions ensuring existence, uniqueness, global exponential stability of the equilibrium point and periodic oscillation of the networks are obtained, which are easy to check and apply in practice. In addition, an example is also given to illustrate the effectiveness of the theory.