Computer security vulnerabilities span across large, enterprise networks and have to be mitigated by security engineers on a routine basis. Presently, security engineers will assess their “risk posture” through quantifying the number of vulnerabilities with a high Common Vulnerability Severity Score (CVSS). Yet, little to no attention is given to the length of time by which vulnerabilities persist and survive on the network. In this paper, we review a novel approach to quantifying the length of time a vulnerability persists on the network, its time-to-death, and predictors of lower vulnerability survival rates. Our contribution is unique in that we apply the cox proportional hazards regression model to real data from an operational IT environment. This paper provides a mathematical overview of the theory behind survival analysis methods, a description of our vulnerability data, and an interpretation of the results.
Active contours are a popular medical image segmentation strategy. However in practice, its accuracy is dependent on
the initialization of the process. The PCNN (Pulse Coupled Neural Network) algorithm developed by Eckhorn to model
the observed synchronization of neural assemblies in small mammals such as cats allows for segmenting regions of
similar intensity but it lacks a convergence criterion. In this paper we report a novel PCNN based strategy to initialize
the zero level contour for automatic brain cropping of T2 weighted MRI image volumes of Long-Evans rats. Individual
2D anatomy slices of the rat brain volume were processed by means of a PCNN and a surrogate image 'signature' was
constructed for each slice. By employing a previously trained artificial neural network (ANN) an approximate PCNN
iteration (binary mask) was selected. This mask was then used to initialize a region based active contour model to crop
the brain region. We tested this hybrid algorithm on 30 rat brain (256*256*12) volumes and compared the results against
manually cropped gold standard. The Dice and Jaccard similarity indices were used for numerical evaluation of the
proposed hybrid model. The highly successful system yielded an average of 0.97 and 0.94 respectively.
Mesh quality is an important factor for stable, repeatable numerical simulations. The Delaunay method is
widely used for creation of 3D tetrahedral meshes. Two-dimensional triangulation via Delaunay exhibits the
mathematical property of maximizing the minimum interior angle. This feature provides excellent quality meshes for a
given node deployment. However, the 3D equivalent of this property, i.e. to maximize the minimum solid angle, is not
assured with 3D Delaunay. The tetrahedron's interior solid angle is directly related to mesh quality, but it is
independent of the Delaunay process. Consequently, sliver elements and poor quality meshes can be created via
Delaunay tetrahedral formation. In this paper, we describe a method for maximizing the minimum solid angle of
tetrahedral meshes by changing the locations of non-boundary nodes. The displacement of nodes uses a gradient-based
approach. The process is iterative and terminates when the mesh quality exceeds a user specified quality or convergence
criterion. The technique is robust. The relocation of vertices is local which avoids significant deformation of the mesh.
The results show considerable improvements in mesh quality. Using a 3D human brain mesh (27,000+ elements), our
algorithm reduced the number of ill-formed elements three fold. We are extending this approach to allow tangential
motion along the boundary surfaces. Currently all boundary nodes are fixed which constrains some of the element
Medical research is dominated by animal models, especially rats and mice. Within a species most laboratory subjects
exhibit little variation in brain anatomy. This uniformity of features is used to crop regions of interest based upon a
known, cropped brain atlas. For any study involving N subjects, image registration or alignment to an atlas is required to
construct a composite result. A highly resolved stack of T2 weighted MRI anatomy images of a Sprague-Dawley rat was
registered and cropped to a known segmented atlas. This registered MRI volume was used as the reference atlas. A Pulse
Coupled Neural Network (PCNN) was used to separate brain tissue from surrounding structures, such as cranium and
muscle. Each iteration of the PCNN produces binary images of increasing area as the intensity spectrum is increased. A
rapid filtering algorithm is applied that breaks narrow passages connecting larger segmented areas. A Generalized
Invariant Hough Transform is applied subsequently to each PCNN segmented area to identify which segmented
reference slice it matches. This process is repeated for multiple slices within each subject. Since we have apriori
knowledge of the image ordering and fields of view this information provides initial estimates for subsequent
registration codes. This process of subject slice extraction to PCNN mask creations and GIHT matching with known
atlas locations is fully automatic.
An automatic 3D non-rigid body registration system based upon the genetic algorithm (GA) process is presented. The system has been successfully applied to 2D and 3D situations using both rigid-body and affine transformations. Conventional optimization techniques and gradient search strategies generally require a good initial start location. The GA approach avoids the local minima/maxima traps of conventional optimization techniques. Based on the principles of Darwinian natural selection (survival of the fittest), the genetic algorithm has two basic steps: 1. Randomly generate an initial population. 2. Repeated application of the natural selection operation until a termination measure is satisfied. The natural selection process selects individuals based on their fitness to participate in the genetic operations; and it creates new individuals by inheritance from both parents, genetic recombination (crossover) and mutation. Once the termination criteria are satisfied, the optimum is selected from the population. The algorithm was applied on 2D and 3D magnetic resonance images (MRI). It does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. To evaluate the performance of the GA registration, the results were compared with results of the Automatic Image Registration technique (AIR) and manual registration which was used as the gold standard. Results showed that our GA implementation was a robust algorithm and gives very close results to the gold standard. A pre-cropping strategy was also discussed as an efficient preprocessing step to enhance the registration accuracy.
In contrast to traditional 'video conferencing' the Access Grid (AG), developed by Argonne National Laboratory, is a collaboration of audio, video and shared application tools which provide the 'persistent presence' of each participant. Among the shared application tools are the ability to share viewing and control of presentations, browsers, images and movies. When used in conjunction with Virtual Network Computing (VNC) software, an investigator can interact with colleagues at a remote site, and control remote systems via local keyboard and mouse commands. This combination allows for effective viewing and discussion of information, i.e. data, images, and results. It is clear that such an approach when applied to the medical sciences will provide a means by which a team of experts can not only access, but interact and control medical devices for the purpose of experimentation, diagnosis, surgery and therapy. We present the development of an application node at our 4.7 Tesla MR magnet facility, and a demonstration of remote investigator control of the magnet. A local magnet operator performs manual tasks such as loading the test subject into the magnet and administering the stimulus associated with the functional MRI study. The remote investigator has complete control of the magnet console. S/he can adjust the gradient coil settings, the pulse sequence, image capture frequency, etc. A geographically distributed audience views and interacts with the remote investigator and local MR operator. This AG demonstration of MR magnet control illuminates the potential of untethered medical experiments, procedures and training.
It is crucial that breast cancer be detected in its earlier and more curable stages of development. New imaging modalities are emerging, such as electrical impedance spectroscopy (EIS), microwave imaging and spectroscopy (MIS), magnetic resonance elastography (MRE), and near-infrared (NIR) imaging. These alternative imaging modalities strive to alleviate limitations of traditional screening and diagnostic tools on dense breast tissue and detection of small abnormalities. The purpose of this study is to combine the results from alternative imaging modalities with T1 and T2-weighted MR Imaging. Two categories of data are presented, pixel data (MRIs) and geometry model with scalar values (MRE and MIS). Three dimensional mesh models (surface/volume meshes) are generated using the automatic mesh generator for biological models developed in the laboratory. A graphic user interface (GUI) for medical image processing powered by Visualization Toolkit (VTK) was developed which supports interactive and automatic image registration, image volume manipulation and geometry rendering. Registration of image/image and image/geometry is a fundamental requirement for multi-spectral data visualization within the same workspace. Various physical properties can be visualized to reveal the correlations between alternative imaging modalities and subsequently for breast tissue classification. A registration strategy was implemented using T1 and T2-weighted MR data as the standard subject. It combined automated image registration (AIR) with interactive registration routines. The final synthetic datasets are rendered in 3D views. This framework was created for multi-modality breast imaging data registration and visualization. The aligned image/geometry data facilitate breast tissue classification.