11507469
Automated extraction of fine features of kinetochore microtubules and plus-ends from electron tomography volume
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.
14650957
Automated Extraction of Microtubules and Their Plus-Ends
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.
15586921
Vortex Visualization for Practical Engineering Applications
In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability
13273751
Robust Document Image Authentication
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.
31603515
Existence and Stability of Periodic Solution of Non-autonomous Neural Networks with Delay
The paper investigates the existence and global stability of periodic solution of non-autonomous neural networks with delay. Then the existence and uniqueness of periodic solutions of the neural networks are discussed in the paper. Moreover, criterion on stability of periodic solutions of the neural networks is obtained by using matrix function inequality, and algorithm for the criterion on the neural networks is provided. Result in the paper generalizes and improves the result in the existing references. In the end, an illustrate example is given to verify our results.
5277394
Cone-beam reconstruction for Micro-CT
X-ray micro-CT has been a focus in the CT/Micro-CT Lab, the University of Iowa. In this paper, some recent progress we made on cone-beam micro-CT is described. First, we address approximation reconstruction using Feldkamp-type algorithms with multiple X-ray sources and displaced detector configurations. Then, we explain three types of artifacts associated with exact reconstruction in the Grangeat framework. Finally, we report the convergence of a generalized block-iterative Landweber scheme. Representative images from simulation and experiments are also given.
42091902
Global stability of periodic solution for bidirectional associative memory neural networks with varying-time delay
This paper investigates the global stability of periodic solution for bidirectional associative memory neural networks by using linear matrix inequality, and presents new sufficient conditions on the existence, uniqueness, global exponential stability and asymptotic stability of periodic solution for bidirectional associative memory neural networks with varying-time delays. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper are generalized and the ones reported in the existing literatures are improved. Numerical simulations are given to verify the effectiveness of our main results.