Nikolaos G. Bourbakis

Wright State University, Dayton, Ohio, United States

Are you Nikolaos G. Bourbakis?

Claim your profile

Publications (240)143.43 Total impact

  • A. Tsitsoulis, N. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Extraction of the human body in single, unconstrained, monocular images is a very difficult task. Localization and extraction of the body region, however, provides important and useful knowledge that can facilitate many other tasks, such as gesture recognition, pose estimation and action recognition. In this paper we present a simple appearance-based methodology that combines face detection, skin detection, image segmentation and anthropometric constraints to efficiently estimate the position and regions of hands in images. It requires no training neither explicit estimation of the human pose. Experimental results in a difficult dataset illustrate the performance of the approach.
    Information, Intelligence, Systems and Applications (IISA), 2013 Fourth International Conference on; 01/2013
  • [show abstract] [hide abstract]
    ABSTRACT: Offering an alternative mode of interaction with the surrounding 3-D space to the visually impaired for collision free navigation is a goal of great significance that includes several key challenges. In this paper, we study the alternative 3-D space sensation that is interpreted by our computer vision prototype system and transferred to the user via a vibration array. There are two main tasks for conducting such a study. The first task is to detect obstacles in close proximity, and motion patterns in image sequences, both important issues for a safe navigation in a 3-D dynamic space. To achieve this task, the images from the left and right cameras are acquired to produce new stereo images, followed by video stabilization as a preprocessing stage, a nonlinear spatio-temporal diffusion and kernel based density estimation method to assess the motion activity, and finally watershed-based detection of moving regions (or obstacles) of interest. The second task is to efficiently represent the information of the captured static and dynamic visual scenes as 3-D detectable patterns of vibrations applied on the human body to create a 3-D sensation of the space during navigation. To accomplish this task, considering the current limitations imposed by the technology, we create a high-to-low (H-L) image resolution representation scheme to facilitate the mapping onto a low-resolution 2-D array of vibrators. The H-L scheme uses pyramidal modeling to obtain low-resolution images of interest-preserving motion and obstacles-that are mapped onto a vibration array. These patterns are utilized to train and test the performance of the users in free space navigation. Thus, in this paper we study the synergy of these two important schemes to offer an alternative sensation of the 3-D space to the visually impaired via an array of vibrators. Particularly, the motion component is employed as an element for the identification of visual information of interest to be retained during the H-L tran- formation. The role of the array vibrators is to create a small-scale front representation of the space via various levels of vibrations. Thus, 3-D vibrations applied on the user's body (chest, abdomen) offer a 3-D sensation of the surrounding space and the motion in it. In addition, we present experimental results that indicate the efficiency of this navigation scheme in creating low-resolution 3-D views of the free navigation space and detecting obstacles and moving areas.
    IEEE Sensors Journal 01/2013; 13(7):2535-2547. · 1.48 Impact Factor
  • Grigorios Chrysos, Apostolos Dollas, Nikolaos Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Image segmentation is one of the first important and difficult steps of image analysis and computer vision and it is considered as one of the oldest problems in machine vision. Lately, several segmentation algorithms have been developed with features related to thresholding, edge location and region growing to offer an opportunity for the development of faster image/video analysis and recognition systems. In addition, fuzzy-based segmentation algorithms have essentially contributed to synthesis of regions for better representation of objects. These algorithms have minor differences in their performance and they all perform well. Thus, the selection of one algorithm vs. another will be based on subjective criteria, or, driven by the application itself. Here, a low-cost embedded reconfigurable architecture for the Fuzzy-like reasoning segmentation (FRS) method is presented. The FRS method has three stages (smoothing, edge detection and the actual segmentation). The initial smoothing operation is intended to remove noise. The smoother and edge detector algorithms are also included in this processing step. The segmentation algorithm uses edge information and the smoothed image to find segments present within the image. In this work the FRS segmentation algorithm was selected due to its proven good performance on a variety of applications (face detection, motion detection, Automatic Target Recognition (ATR)) and has been developed in a low-cost, reconfigurable computing platform, aiming at low cost applications. In particular, this paper presents the implementation of the smoothing, edge detection and color segmentation algorithms using Stretch S5000 processors and compares them with a software implementation using the Matlab. The new architecture is presented in detail in this work, together with results from standard benchmarks and comparisons to alternative technologies. This is the first such implementation that we know of, having at the same time high throughput, excellent performance (at least in standard benchmarks) and low cost.
    Microprocessors and Microsystems 05/2012; 36(3):215–231. · 0.55 Impact Factor
  • A. Tsitsoulis, R. Patrick, N. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: A surveillance system for assisting the elderly in remaining independent in their familiar environment is one of the subjects interest in recent healthcare studies. When mature, it is expected that this system will have the ability to track objects that a resident may lose periodically, detect falls within the home, alert family members or healthcare professionals to abnormal behaviors. This paper addresses the early stages and issues of the development of such a system, the physical characteristics of the system that is being designed, early results, and guidance on the future work that will have to be completed in the future.
    Tools with Artificial Intelligence (ICTAI), 2012 IEEE 24th International Conference on; 01/2012
  • A. Karargyris, N. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Over the last decade, wireless capsule endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Physicians using WCE can examine the digestive tract in a minimally invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers, and Crohn's disease. To improve effectiveness of WCE, researchers have developed software methods to automatically detect these diseases at a high rate of success. This paper proposes a novel synergistic methodology for automatically discovering polyps (protrusions) and perforated ulcers in WCE video frames. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.
    IEEE Transactions on Biomedical Engineering 11/2011; · 2.35 Impact Factor
  • Raghudeep Kannavara, Sukarno Mertoguno, Nikolaos Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: This paper presents the design of the SCAN secure processor and its extended instruction set to enable secure biometric authentication. The SCAN secure processor is a modified SparcV8 processor architecture with a new instruction set to handle voice, iris, and fingerprint-based biometric authentication. The algorithms for processing biometric data are based on the local global graph methodology. The biometric modules are synthesized in reconfigurable logic and the results of the field-programmable gate array (FPGA) synthesis are presented. We propose to implement the above-mentioned modules in an off-chip FPGA co-processor. Further, the SCAN-secure processor will offer a SCAN-based encryption and decryption of 32 bit instructions and data.
    Journal of Electronic Imaging 04/2011; 20(2). · 1.06 Impact Factor
  • N. Bourbakis, A. Tsitsoulis
    [show abstract] [hide abstract]
    ABSTRACT: Image segmentation is one of the first important parts of the image analysis and understanding. Theo Pavlidis initially introduced it in late 60s, and since then researchers have studied it and produced a great variety of algorithms. These algorithms provide segmentation results that could be characterized from “objective”, like Pavlidis segmentation to “subjective” ones dependent on the interpretation of the human user. In this paper we attempt to initiate a first stage evaluation of color image segmentation methodologies by proposing a simple segmentation metric. In particular, we have used three segmentation algorithms on different types of images (human faces/bodies, natural environments, and structures (buildings). Then we apply the metric (formula) on these segmented results in order to quickly observe and evaluate the performance (behavior) of each segmentation methodology.
    01/2011;
  • Alexandros Pantelopoulos, Nikolaos G. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Wearable health monitoring systems (WHMS) enable ubiquitous and unobtrusive monitoring of a variety of vital signs that can be measured non-invasively. These systems have the potential to revolutionize healthcare delivery by achieving early detection of critical health changes and thus possibly even disease or hazardous event prevention. Amongst the patient populations that can greatly benefit from WHMS are Congestive Heart Failure (CHF) patients. For CHF management the detection of heart arrhythmias is of crucial importance. However, since WHMS have limited computing and storage resources, diagnostic algorithms need to be computationally inexpensive. Towards this goal, we investigate in this paper the efficiency of the Matching algorithm in deriving compact time-frequency representations of ECG data, which can then be utilized from an Artificial Neural Network (ANN) to achieve beat classification. In order to select the most appropriate decomposition structure, we examine the effect of the type of dictionary utilized (stationary wavelets, cosine packets, wavelet packets) in deriving optimal features for classfication. Our results show that by applying a greedy algorithm to determine the dictionary atoms that show the greatest correlation with the ECG morphologies, an accurate, efficient and real-time beat classification scheme can be derived. Such an algorithm can then be inexpensively run on a resource-constrained portable device such as a cell phone or even directly on a smaller microcontroller-based board. The performance of our approach is evaluated using the MIT-BIH Arrhythmia database. Provided results illustrate the accuracy of the proposed method (94.9%), which together with its simplicity (a single linear transform is required for feature extraction) justify its use for real-time classification of abnormal heartbeats on a portable heart monitoring system.
    IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011; 01/2011
  • N. Bourbakis, I. Papadakis-Ktistakis
    [show abstract] [hide abstract]
    ABSTRACT: The rapid deployment of rescue teams for saving lives in natural or artificial disasters is critically depended by the information collected from the disaster area. There are different types of information (visual, audio, smelling, other) collected from the disastrous areas. Rescuing humans under debris is a very difficult task for the first responders due to several constraints, such as time limitation, the unknown structure of the destroyed buildings, the difficulty to collect information beneath the structures, etc. The collection of information beneath destroyed structures requires the accurate description of the D representation of the space and the correct location of the human subject under the debris. This paper deals with the design of a micro‐robot, called Thiseas, capable to select visual and audio information beneath destroyed buildings and locate human subjects in areas (like small holes or deep underground cavities) that other devices (like large robots) or equipment (like ultra sounds) don't offer.
    01/2011;
  • Source
    Stejskal V, Bourbakis N, Anna Esposito
    [show abstract] [hide abstract]
    ABSTRACT: This work describes two new pause detection algorithms and compare their performance with four standard Voice Activity Detection (VAD) methods represented by the adaptive Long Term Spectral Divergence (LTSD) algorithm, the Likelihood Ratio Test (LRT) algorithm, the Neural Network thresholding and G.729.
    Journal of Advanced Computational Intelligence and Intelligent Informatics 01/2011; 2(1):145-160.
  • Nikolaos G. Bourbakis, Sokratis Makrogiannis, D. Kapogiannis
    [show abstract] [hide abstract]
    ABSTRACT: It is known that the early detection of chronic diseases significantly increases the life span of the elderly and improves the quality of life in general. Since the brain is the most valuable part of the human body, it is very important for the physicians to know the stages of aging and changes that take place during these stages and possible implications associated with them in order to more effectively treat their patients. In addition, fMRI provides information (size, density, location, etc) from brain images for the active regions during thinking and/or performing certain tasks. Thus, in this paper a monitoring brain-aging model based on a synergy of methodologies, like image segmentation, registration, local global (L-G) graphs and stochastic Petri net (SPN) graphs is presented. In particular, the synergistic brain-aging model uses fMRI images to detect, extract and associate the way that the brain regions interact regarding thinking and/or executing certain tasks. These brain-region images are extracted and geometrically are represented and associated with the L-G graphs. Then the use of SPN graphs models the regions' functionality. Thus, comparing the L-G and SPN graphs extracted from fMRI images taken in different periods from the same subject, the model has the capability to detect changes and associate them in order the medical expert to monitor the health status and provide a diagnosis or prognosis regarding a human subject. Sets of L-G and SPN graph models generated in time for each particular subject are available in an L-G/SPN graph Database. Here the synergistic model and its proof of concept are presented.
    IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011; 01/2011
  • Alexandros Karargyris, Nikolaos G. Bourbakis
    IEEE Trans. Med. Imaging. 01/2011; 30:957-971.
  • Alexandros Karargyris, Nikolaos Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Wireless capsule endoscopy is a revolutionary technology that allows physicians to examine the digestive tract of a human body in the minimum invasive way. Physicians can detect diseases such as blood-based abnormalities, polyps, ulcers, and Crohn's disease. Although this technology is really a marvel of our modern times, currently it suffers from two serious drawbacks: 1) frame rate is low (3 frames/s) and 2) no 3-D representation of the objects is captured from the camera of the capsule. In this paper we offer solutions (methodologies) that deal with each of the above issues improving the current technology without forcing hardware upgrades. These methodologies work synergistically to create smooth and visually friendly interpolated images from consecutive frames, while preserving the structure of the observed objects. They also extract and represent the texture of the surface of the digestive tract in 3-D. Thus the purpose of our methodology is not to reduce the time that the gastroenterologists need to spend to examine the video. On the contrary, the purpose is to enhance the video and therefore improve the viewing of the digestive tract leading to a more qualitative and efficient examination. The proposed work introduces 3-D capsule endoscopy textured results that have been welcomed by Digestive Specialists, Inc., Dayton, OH. Finally, illustrative results are given at the end of the paper.
    IEEE transactions on medical imaging. 12/2010; 30(4):957-71.
  • N. Bourbakis, G. Giakos, A. Karargyris
    [show abstract] [hide abstract]
    ABSTRACT: This paper presents the design of two (2) novel endoscopic capsules. Using current capsule endoscopy technologies approximately 70,000 images of the human gastric tract are obtained during the examination. Usually, these images are reviewed by a gastroenterologist in a form of a video at various speeds (5 - 40 frames/sec). A physician spends 55 to 180 minutes reading these results. This represents a major problem for the gastroenterologist because it consumes a lot of time. Usually, it takes several days for the examination results to become available since the physician has to find the time frame to study each video uninterrupted for up to 3 hours. Researchers and companies have developed software methodologies for speeding up the examination process by reducing the number of frames per video. In addition, several new designs have been proposed for more efficient and flexible capsules. These designs offer potential solutions for the endoscopy field. In this paper we present our novel designs for two flexible capsules, a diagnostic capsule and a therapeutic capsule. The diagnostic capsule enhances the imaging characteristics of the intestine and achieves more accurate diagnoses. The therapeutic capsule is fully controllable, moveable and is able to collect specimens from a suspicious area for a quick biopsy. The design of these capsules is based on features selected from in house software methodologies, as well as characteristics of the small intestine such as peristalsis.
    Imaging Systems and Techniques (IST), 2010 IEEE International Conference on; 08/2010
  • Alexandros Pantelopoulos, Nikolaos G Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Wearable health-monitoring systems (WHMSs) represent the new generation of healthcare by providing real-time unobtrusive monitoring of patients' physiological parameters through the deployment of several on-body and even intrabody biosensors. Although several technological issues regarding WHMS still need to be resolved in order to become more applicable in real-life scenarios, it is expected that continuous ambulatory monitoring of vital signs will enable proactive personal health management and better treatment of patients suffering from chronic diseases, of the elderly population, and of emergency situations. In this paper, we present a physiological data fusion model for multisensor WHMS called Prognosis. The proposed methodology is based on a fuzzy regular language for the generation of the prognoses of the health conditions of the patient, whereby the current state of the corresponding fuzzy finite-state machine signifies the current estimated health state and context of the patient. The operation of the proposed scheme is explained via detailed examples in hypothetical scenarios. Finally, a stochastic Petri net model of the human-device interaction is presented, which illustrates how additional health status feedback can be obtained from the WHMS' user.
    IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 05/2010; 14(3):613-21. · 1.69 Impact Factor
  • A. Karargyris, N. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: The purpose of this study was to serve as a reference for further research improvements and to address the current level of maturity of each methodology from its maximum capabilities. This paper discussed the following: automatic detection of intestinal juices in wireless capsule video endoscopy; neural networks-based approach; model of deformable rings for aiding the WCE video interpretation and reporting; digestive organ automatic image classification; WCE blood detection using expectation maximization clustering; discriminate tissues color distributions; color- and texture-based GI tissue discrimination; topographic segmentation and transit time estimation for endoscopic capsule exams; automated tissue classification; colonoscopic diagnosis using online learning and differential evolution; computer-aided tumor detection using color wavelet features; versatile coLD detection system for colorectal lesions; images sequences; blood-based abnormalities detection; and other related topics.
    IEEE Engineering in Medicine and Biology Magazine 03/2010; · 26.30 Impact Factor
  • Source
    A. Pantelopoulos, N.G. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.
    IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) 02/2010; · 2.55 Impact Factor
  • Alexandros Pantelopoulos, Nikolaos Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: In this paper we employ the Matching Pursuit algorithm in order to obtain compact time-frequency representations of ECG data, which are then utilized from an ANN to achieve beat classification. To obtain optimum performance, the effect of the following attributes on the classification performance is examined: number of atoms, type of wavelet and number of ECG samples around the R peak. Our goal is to derive an accurate, efficient and real-time beat classification scheme, which could then be implemented on a resource-constrained portable device such as a cell phone. The proposed scheme is based on an existing beat classification method, but has the following favorable attributes: it utilizes less features, a single ECG lead and also only a single MLP in order to be able to discriminate between various abnormal beats. The performance of our approach is evaluated using the MIT-BIH Arrhythmia database. Provided results illustrate the accuracy of the proposed method (98.7%), which together with its simplicity (a single linear transform is required for feature extraction) justify its use for real-time classification of abnormal heartbeats on a portable heart monitoring system.
    01/2010;
  • Alexandros Karargyris, Nikolaos G. Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Wireless Capsule Endoscopy (WCE) is a popular diagnostic technology that enables gastroenterologists to view the human digestive tract and more particularly, the small bowel, searching for various abnormalities like blood-based abnormalities, ulcers and polyps. This technology captures videos that consist of approximately 50,000 frames making its examination a very tedious task. For power consumption reasons the rate at which the frames are taken is extremely low (3 frames / second). This has a negative effect on the smoothness of the captured video and the consistency of the observed objects. This paper proposes a sophisticated video interpolation methodology for creating smooth and visually pleasing intermediate images from consecutive frames, while preserving the structure of the observed objects. It utilizes techniques and concepts from various fields such as optical flow and elasticity. Illustrative results of the methodology are also given in this paper.
    10th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2010, Philadelphia, Pennsylvania, USA, May 31-June 3 2010; 01/2010
  • Alexandros Karargyris, Nikolaos Bourbakis
    [show abstract] [hide abstract]
    ABSTRACT: Wireless capsule endoscopy (WCE) is a recently established methodology that offers to medical doctors (gastroenterologists) the capability to examine the interior of the small intestine with a noninvasive procedure. Before the introduction of WCE, it was impossible for a physician to examine tissues of the small intestine without performing a surgical operation. Although WCE has the advantage of investigating the whole digestive system, the viewing and evaluation of each WCE video is a time-consuming process (2-3 h) for MD gastroenterologists. This makes the WCE methodology not widely efficient and acceptable by MDs.
    IEEE Engineering in Medicine and Biology Magazine 01/2010; 29(1):72-83. · 26.30 Impact Factor

Publication Stats

1k Citations
293 Downloads
143.43 Total Impact Points

Institutions

  • 2001–2013
    • Wright State University
      • Department of Computer Science and Engineering
      Dayton, Ohio, United States
  • 2003–2012
    • Technical University of Crete
      • Department of Electronic and Computer Engineering
      La Canée, Crete, Greece
  • 2009
    • University of Toronto
      Toronto, Ontario, Canada
    • Jacksonville State University
      Alabama, United States
    • King's College London
      Londinium, England, United Kingdom
  • 2004–2008
    • University of Nevada, Reno
      • Department of Computer Science and Engineering
      Reno, NV, United States
  • 2005
    • Wayne State University
      Detroit, Michigan, United States
  • 1993–2001
    • Binghamton University
      • • Department of Electrical and Computer Engineering
      • • Center for Intelligent Systems
      Binghamton, New York, United States
  • 1995–1999
    • State University of New York
      New York City, New York, United States
  • 1997
    • Purdue University
      • School of Nuclear Engineering
      West Lafayette, IN, United States
  • 1996
    • University of Crete
      Retimo, Crete, Greece
  • 1990–1991
    • IBM
      Armonk, New York, United States
    • Australian National University
      Canberra, Australian Capital Territory, Australia
  • 1988–1989
    • George Mason University
      • Department of Electrical and Computer Engineering
      Fairfax, VA, United States