Akmal A. Younis

Automatic moving object extraction for content-based applications

Rapid developments in the Internet and multimedia applications allow us to access large amounts of image and video data. While significant progress has been made in digital data compression, content-based functionalities are still quite limited. Many existing techniques in content-based retrieval are based on global visual features extracted from the entire image. In order to provide more efficient content-based functionalities for video applications, it is necessary to extract meaningful video objects from scenes to enable object-based representation of video content. Object-based representation is also introduced by MPEG-4 to enable content-based functionality and high coding efficiency. In this paper, we propose a new algorithm that automatically extracts meaningful video objects from video sequences. The algorithm begins with the robust motion segmentation on the first two successive frames. To detect moving objects, segmented regions are grouped together according to their spatial similarity. A binary object model for each moving object is automatically derived and tracked in subsequent frames using the generalized Hausdorff distance. The object model is updated for each frame to accommodate for complex motions and shape changes of the object. Experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of our proposed algorithm.

Hidden Markov models-based 3D MRI brain segmentation

Abstract This paper introduces a 3D MRI segmentation algorithm based on Hidden Markov Models (HMMs). The mathematical models for the HMM that forms the basis of the segmentation algorithm for both the continuous and discrete cases are developed and contrasted with Hidden Markov Random Field in terms of complexity and extensibility to larger fields. The presented algorithm clearly demonstrates the capacity of HMM to tackle multi-dimensional classification problems. The HMM-based segmentation algorithm was evaluated through application to simulated brain images from the McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University as well as real brain images from the Internet Brain Segmentation Repository (IBSR), Harvard University. The HMM model exhibited high accuracy in segmenting the simulated brain data and an even higher accuracy when compared to other techniques applied to the IBSR 3D MRI data sets. The achieved accuracy of the segmentation results is attributed to the HMM foundation and the utilization of the 3D model of the data. The IBSR 3D MRI data sets encompass various levels of difficulty and artifacts that were chosen to pose a wide range of challenges, which required handling of sudden intensity variations and the need for global intensity level correction and 3D anisotropic filtering. During segmentation, each class of MR tissue was assigned to a separate HMM and all of the models were trained using the discriminative MCE training algorithm. The results were numerically assessed and compared to those reported using other techniques applied to the same data sets, including manual segmentations establishing the ground truth for real MR brain data. The results obtained using the HMM-based algorithm were the closest to the manual segmentation ground truth in terms of an objective measure of overlap compared to other methods.

Top-k generation of integrated schemas based on directed and weighted correspondences

Schema integration is the problem of creating a unified target schema based on a set of existing source schemas and based on a set of correspondences that are the result of matching the source schemas. Previous methods for schema integration rely on the exploration, implicit or explicit, of the multiple design choices that are possible for the integrated schema. Such exploration relies heavily on user interaction; thus, it is time consuming and labor intensive. Furthermore, previous methods have ignored the additional information that typically results from the schema matching process, that is, the weights and in some cases the directions that are associated with the correspondences. In this paper, we propose a more automatic approach to schema integration that is based on the use of directed and weighted correspondences between the concepts that appear in the source schemas. A key component of our approach is a novel top-k ranking algorithm for the automatic generation of the best candidate schemas. The algorithm gives more weight to schemas that combine the concepts with higher similarity or coverage. Thus, the algorithm makes certain decisions that otherwise would likely be taken by a human expert. We show that the algorithm runs in polynomial time and moreover has good performance in practice.

An Artificial Immune-Activated Neural Network Applied to Brain 3D MRI Segmentation

In this paper, a new neural network model inspired by the biological immune system functions is presented. The model, termed Artificial Immune-Activated Neural Network (AIANN), extracts classification knowledge from a training data set, which is then used to classify input patterns or vectors. The AIANN is based on a neuron activation function whose behavior is conceptually modeled after the chemical bonds between the receptors and epitopes in the biological immune system. The bonding is controlled through an energy measure to ensure accurate recognition. The AIANN model was applied to the segmentation of 3-dimensional magnetic resonance imaging (MRI) data of the brain and a contextual basis was developed for the segmentation problem. Evaluation of the segmentation results was performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results demonstrated that the AIANN model attained higher average results than those obtained using published methods for real MRI data and simulated MRI data, especially at low levels of noise.

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

Co-segmentation of MR and MR spectroscopy imaging using hidden markov models

A Hidden Markov Models based technique is introduced for co-segmentation of MRI and MRSI data of the brain. The technique demonstrates the ability of Hidden Markov Models to handle the co-analysis of MRI and MRSI for the purpose of improving the accuracy of MRI segmentation as well as the quantification of brain metabolites. For that purpose, two HMM-based schemes are presented; one that relies on parallel HMMs for separately analyzing MRI and MRSI data and the other utilizes combined feature vectors of MRI and MRSI data. The co-segmentation of MRI and MRSI data using HMMs is evaluated using simulated MRI brain data (from the McConnell Brain Imaging Centre, Montreal Neurological Institute of McGill University) and simulated MRSI data. Experimental results demonstrate that the co-segmentation of brain MRI and MRSI data based on HMMs exhibited higher accuracy, in terms of the Dice similarity coefficient, than only using brain MRI data. The technique involving parallel HMMs that separately analyze brain MRI and MRSI data and then combine the segmentation results demonstrated better accuracy and faster segmentation times compared to the co-analysis of combined MRI and MRSI data of the brain.

A Proactive Fault Tolerance Management Algorithm for Mobile Ad Hoc Networks

Mobile Ad hoc networking is evolving to form the basis for future military and disaster relief network architecture. In such high dynamic networks, such Mobile Ad hoc NETworks (MANETs) are formed over wireless links that are susceptible to failure. Strict requirements on managing security and reliability combined with the dynamic nature of the network provide a strong motivation for proactive self- organizing, self configuring, and self healing management capabilities in the network. This paper describes a Proactive Management Algorithm (PMA) for MANETs. PMA is based on the effective integration of autonomous, predictive and adaptive distributed management strategies to provide proactive fault tolerance management algorithm. Proactive management is achieved through the distributed analysis of the current performance of the mobile nodes utilizing an optimistic discrete event simulation method, which is used to predict the mobile nodes future status and to execute a proactive fault tolerant management scheme. PMA take advantage of distributed parallel processing, flexibility and intelligence of active packets to minimize the management overhead, while adapting to the highly dynamic and resource-constrained nature of MANETs. The simulation results demonstrate that PMA not only significantly reduce management control overhead, but also substantially improves both the performance and the stability of the MANETs

MS Lesions Detection in MRI Using Grouping Artificial Immune Networks

A dual-channel 3D MRI segmentation technique based on grouping artificial immune networks (GAIN) is introduced to detect MS lesion in MR images. The technique demonstrates the ability of artificial immune networks to handle MS lesions detection in T1- and T2-weighted brain MRI. The GAIN-based MRI segmentation technique was evaluated using simulated MS brain images from the McConnell Brain Imaging Centre, Montreal Neurological Institute of McGill University. 3D anisotropic filtering is used to handle noise artifacts in the simulated 3D MRI data sets. Experimental results demonstrated that dual channel MS segmentation approach exhibited high accuracy in segmenting the simulated MS brain data and an even higher accuracy when compared to techniques based on single channel 3D MRI data sets in terms of the Dice coefficient, an objective measure of overlap.

High‐accuracy hierarchical parallel technique for hidden Markov model‐based 3D magnetic resonance image brain segmentation

Scientific applications represent a dominant sector of compute‐intensive applications. Using massively parallel processing systems increases the feasibility to automate such applications because of the cooperation among multiple processors to perform the designated task. This paper proposes a parallel hidden Markov model (HMM) algorithm for 3D magnetic resonance image brain segmentation using two approaches. In the first approach, a hierarchical/multilevel parallel technique is used to achieve high performance for the running algorithm. This approach can speed up the computation process up to 7.8× compared with a serial run. The second approach is orthogonal to the first and tries to help in obtaining a minimum error for 3D magnetic resonance image brain segmentation using multiple processes with different randomization paths for cooperative fast minimum error convergence. This approach achieves minimum error level for HMM training not achievable by the serial HMM training on a single node. Then both approaches are combined to achieve both high accuracy and high performance simultaneously. For 768 processing nodes of a Blue Gene system, the combined approach, which uses both methods cooperatively, can achieve high‐accuracy HMM parameters with 98% of the error level and 2.6× speedup compared with the pure accuracy‐oriented approach alone. Copyright

Hierarchical-based parallel technique for HMM 3D MRI brain segmentation algorithm

This paper proposes a hidden Markov model (HMM) algorithm for 3D MRI brain segmentation using a hierarchical/multi-level parallel implementation. The new technique is implemented using standard message passing interface (MPI). Two platforms are used to test the proposed technique namely PC-cluster system and IBM Blue Gene (BG)/L system. On PC-cluster system, hierarchical-based parallel HMM algorithm achieves a twofold speedup on a three nodes cluster and a threefold speedup on a six nodes cluster. Communication overhead and data dependency nullify any speedup beyond six nodes. On IBM BG/L system, the high-speed communication network and optimised MPI allow more efficient processing nodes utilisation although the algorithm data dependency limits the net speedup achieved.