A Study of a Ventricular Motion in Cardiac MRI using Deformable Model

We experimented with a novel deformable model that track the right ventricle’s (RV) wall motion through complete cardiac cycle by using a snake-like approach. The model uses a complex Fourier shape descriptor parameterization for efficient calculation of forces that constrains contour deformation. Even though the complexity exists in RV boundary shape, the model tracks the contour correctly and shows the robustness in weak contrast and noisy edge map. We also present a quantitative evaluation of delineation accuracy by comparing manual segmented contours and semi-automatically segmented contour, to check the reliability of our deformable model. The extracted shapes shows that the error between two contours to be an average of two pixels from 256 pixels by 256 pixels of cardiac magnetic resonance images. We used the spatio-temporal characterization of ventricular wall motion, obtained by our model, to help classifying the Intra-ventricular dyssynchrony (IVD) in the LV - i.e. asynchronous activation of LV wall - by adding RV information of ventricular movement to existing data. The classifying method was to use a popular statistical pattern recognition method of the Principal Component Analysis and the Fisher’s Linear Discriminant Analysis. From a database contains 33 patients, our classifier produced correct classification performance of 87.9 % with the RV data, which shows the promising improved IVD classification as contrast to current criteria for selecting therapy, which provided the correct classification of just 84.8 % on the same database with only the LV data

Phoenix: DGA-Based Botnet Tracking and Intelligence

Abstract. Modern botnets rely on domain-generation algorithms (DGAs) to build resilient command-and-control infrastructures. Given the prevalence of this mechanism, recent work has focused on the anal-ysis of DNS traffic to recognize botnets based on their DGAs. While previous work has concentrated on detection, we focus on supporting intelligence operations. We propose Phoenix, a mechanism that, in ad-dition to telling DGA- and non-DGA-generated domains apart using a combination of string and IP-based features, characterizes the DGAs behind them, and, most importantly, finds groups of DGA-generated domains that are representative of the respective botnets. As a result, Phoenix can associate previously unknown DGA-generated domains to these groups, and produce novel knowledge about the evolving behavior of each tracked botnet. We evaluated Phoenix on 1,153,516 domains, in-cluding DGA-generated domains from modern, well-known botnets: with-out supervision, it correctly distinguished DGA- vs. non-DGA-generated domains in 94.8 percent of the cases, characterized families of domains that belonged to distinct DGAs, and helped researchers “on the field” in gathering intelligence on suspicious domains to identify the correct botnet.

EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering

The Gaussian mixture model (GMM) provides a convenient yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the theoretical framework of belief functions to better characterize cluster-membership uncertainty. With a mass function representing the cluster membership of each object, the evidential Gaussian mixture distribution composed of the components over the powerset of the desired clusters is proposed to model the entire dataset. The parameters in EGMM are estimated by a specially designed Expectation-Maximization (EM) algorithm. A validity index allowing automatic determination of the proper number of clusters is also provided. The proposed EGMM is as convenient as the classical GMM, but can generate a more informative evidential partition for the considered dataset. Experiments with synthetic and real datasets demonstrate the good performance of the proposed method as compared with some other prototype-based and model-based clustering techniques