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We propose a technique for the automated detection of malignant masses in screening mammography. The technique is based on the presence of concentric layers surrounding a focal area with suspicious morphological characteristics and low relative incidence in the breast region. Mammographic locations with high concentration of concentric layers with(More)
In this paper, we describe a swarm-based fuzzy logic control (FLC) mobile sensor network approach for collaboratively locating the hazardous contaminants in an unknown large-scale area. The mobile sensor network is composed of a collection of distributed nodes (robots), each of which has limited sensing, intelligence and communication capabilities. An(More)
We provide a biasing expansion swarm approach (BESA) for using multiple simple mobile agents, with limited sensing and communication capabilities, to collaboratively search and locate an indeterminate number of emission sources in an unknown large-scale area. The key concept in this approach is swarm behavior. By applying the three properties of the swarm(More)
In the real world, we have to frequently deal with searching for and tracking an optimal solution in a dynamic environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the solution in a dynamic environment. Particle swarm optimization (PSO) is a population-based stochastic optimization technique, which(More)
The K distribution is an accurate model for ultrasonic backscatter. A neural approach is developed to estimate K distribution parameters. Accuracy and consistency of the estimates from simulated K and envelope data compare favorably with other techniques. Neural networks can potentially be used as a complementary technique for tissue characterization.
In this paper, we introduce a novel hierarchical approach for multiphase image segmentation. The approach presents a unified framework that unifies two basic segmentation approaches; level set methods and graph cut algorithms. In the work of El-Zehiry et al. (2007), we have presented a bimodal image segmentation approach that have the advantages of the(More)
Minicolumns are thought to be the smallest cortical modules within the hierarchical organization of the isocortex. Several reports suggest alterations in minicolumnar morphometry may be involved in psychiatric disorders such as autism, dyslexia, and schizophrenia. Thus far anatomical studies of minicolumns have primarily relied on measurements of pyramidal(More)
Information theoretic similarity metrics, including mutual information, have been widely and successfully employed in multimodal biomedical image registration. These metrics are generally based on the Shannon-Boltzmann-Gibbs definition of entropy. However, other entropy definitions exist, including generalized entropies, which are parameterized by a real(More)