Adel Said Elmaghraby

An approach to multimodal biomedical image registration utilizing particle swarm optimization

Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniques frequently fail because functions of these metrics with respect to transformation parameters are generally nonconvex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for single-slice 3D-to-3D biomedical image registration. A new hybrid particle swarm technique is proposed that incorporates initial user guidance. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity metric were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations than the evolutionary strategies in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.

A Concentric Morphology Model for the Detection of Masses in Mammography

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 progressively lower average intensity are considered suspicious deviations from normal parenchyma. The multiple concentric layers (MCLs) technique was trained and tested using the craniocaudal views of 270 mammographic cases with biopsy proven malignant masses from the digital database of screening mammography. One-half of the available cases were used for optimizing the parameters of the detection algorithm. The remaining cases were used for testing. During testing, malignant masses were detected with 92%, 88%, and 81% sensitivity at 5.4, 2.4, and 0.6 false positive marks per image. Testing on 82 normal screening mammograms showed a false positive rate of 5.0, 1.7, and 0.2 marks per image at the previously reported operating points. Furthermore, additional evaluation on 135 benign cases produced a significantly lower detection rate for benign masses (61.6%, 58.3%, and 43.7% at 5.1, 2.8, and 1.2 false positives per image, respectively). Overall, MCL is a promising computer-assisted detection strategy for screening mammograms to identify malignant masses while maintaining the detection rate of benign masses considerably lower.

Cyber security challenges in Smart Cities: Safety, security and privacy

The world is experiencing an evolution of Smart Cities. These emerge from innovations in information technology that, while they create new economic and social opportunities, pose challenges to our security and expectations of privacy. Humans are already interconnected via smart phones and gadgets. Smart energy meters, security devices and smart appliances are being used in many cities. Homes, cars, public venues and other social systems are now on their path to the full connectivity known as the “Internet of Things.” Standards are evolving for all of these potentially connected systems. They will lead to unprecedented improvements in the quality of life. To benefit from them, city infrastructures and services are changing with new interconnected systems for monitoring, control and automation. Intelligent transportation, public and private, will access a web of interconnected data from GPS location to weather and traffic updates. Integrated systems will aid public safety, emergency responders and in disaster recovery. We examine two important and entangled challenges: security and privacy. Security includes illegal access to information and attacks causing physical disruptions in service availability. As digital citizens are more and more instrumented with data available about their location and activities, privacy seems to disappear. Privacy protecting systems that gather data and trigger emergency response when needed are technological challenges that go hand-in-hand with the continuous security challenges. Their implementation is essential for a Smart City in which we would wish to live. We also present a model representing the interactions between person, servers and things. Those are the major element in the Smart City and their interactions are what we need to protect.

A swarm approach for emission sources localization

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 behavior: separation, cohesion and alignment, our approach can ensure the agent group attains dynamically stable ad-hoc connectivity and fast target convergence. Using a grid map to represent the unknown environment, an ad-hoc network for wireless communication and our biasing expansion algorithm for path planning, each agent simultaneously considers all concentration values collected by other swarm members and determines the positive gradient direction of the whole coverage area of the swarm. This will make the swarm immune to the random sensor errors, local aerosol accumulations and other local interference effects during their search. We present a simulated environment that has multiple emission sources and complex aerosol accumulation and distribution. Based on the simulation, our approach can achieve better performance than the gradient descent approach, which currently appears to be the most popular algorithm for emission source localization.

An analytical model for hybrid checkpointing in time warp distributed simulation

The Time Warp distributed simulation algorithm uses checkpointing to save process states after certain event executions for later recovery at the time of a rollback. Two main techniques have been used for checkpointing: periodic state saving and incremental state saving. The former technique introduces large overheads in reconstructing a desired state by coasting forward from an earlier checkpointed state if the computational granularity is large. The latter technique also has large overheads in applications with large rollback distances. A hybrid checkpointing technique is proposed which uses both periodic and incremental state saving simultaneously in such a way that it reduces checkpointing time overheads. A detailed analytical model is developed for the hybrid technique, and comparisons are made using similar analytical models with periodic and incremental state saving techniques. Results show that when the system parameters are chosen to represent large and complex simulated systems, the hybrid approach has less checkpointing time overhead than the other two techniques.

Estimation of K distribution parameters using neural networks

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.

Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration

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 number. New similarity metrics can be derived by exploiting the additivity and pseudoadditivity properties of these entropies. In many cases, use of these measures results in an increased percentage of correct registrations. Results suggest that generalized information theoretic similarity metrics, used in conjunction with other measures, including Shannon entropy metrics, can improve registration performance.

A graph cut based active contour for multiphase image segmentation

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 level set methods such as robustness to noise, blurred edges and topology changes and the advantages of graph cuts vis-a-vis global optimization and speed. Our main objective in this paper is to extend our previous approach to segment n classes. The results section will show that our algorithm outperforms the multiphase image segmentation approach introduced in the work of Vese and Chan (2002).

General ultrasound speckle models in determining scatterer density

In medical ultrasonography, speckle model parameters are dependent on scatterer density and regularity, and can be exploited for use in tissue characterization. The purpose of the current study is to quantify the goodness-of-fit of two models (the Nakagami and K distributions), applied to envelope data representing a range of clinically relevant scattering conditions. Ground truth data for computing goodness-of-fit were generated with envelope simulators. In the first simulation, 100 datasets of various sample sizes were generated with 40 scatterer densities, ranging from 0.025 to 20. Kolmogorov-Smirnov significance values quantified the goodness-of-fit of the two models. In the second simulation, densities ranged from 2 to 60, and additional scattering parameters were allowed to vary. Goodness-of-fit was assessed with four statistical tests. Although the K distribution has a firm physical foundation as a scattering model, inaccuracy and high standard deviation of parameter estimates reduced its effectiveness, especially for smaller sample sizes. In most cases, the Nakagami model, whose parameters are relatively easy to compute, fit the data best, even for large scatterer densities.

Combinatorial Optimization of the piecewise constant Mumford-Shah functional with application to scalar/vector valued and volumetric image segmentation

Front propagation models represent an important category of image segmentation techniques in the current literature. These models are normally formulated in a continuous level sets framework and optimized using gradient descent methods. Such formulations result in very slow algorithms that get easily stuck in local solutions and are highly sensitive to initialization. In this paper, we reformulate one of the most influential front propagation models, the Chan-Vese model, in the discrete domain. The graph representability and submodularity of the discrete energy function is established and then max-flow/min-cut approach is applied to perform the optimization of the discrete energy function. Our results show that this formulation is much more robust than the level sets formulation. Our approach is not sensitive to initialization and provides much faster solutions than level sets. The results also depict that our segmentation approach is robust to topology changes, noise and ill-defined edges, i.e., it preserves all the advantages associated with level sets methods.