Ali Selamat

Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images

Display Omitted We propose a hybrid segmentation modelbased on FCM and kNN algorithm.Outlier has been removed from NC and DC images.Five algorithms have been proposed to extract significant features using VH-IVUS image.Ensemble classifier is used to detect vulnerable plaque. Thin cap fibroatheroma (TCFA) or vulnerable plaque is responsible for the majority of coronary artery death. Virtual Histology Intravascular Ultrasound (VH-IVUS) image is a clinically available method for visualizing color coded tissue maps. However, this technique has considerable limitations in providing medical relevant information for identifying vulnerable plaque. The aim of this paper is to improve the identification of TCFA in VH-IVUS image. Therefore, this paper proposes a set of algorithms for segmentation, feature extraction, and plaque type classification to accurately identify TCFA. A hybrid model using the FCM and kNN (HFCM-kNN) is proposed to accurately segment the VH-IVUS image. The proposed technique is capable of eliminating outliers and detecting clusters with different densities in VH-IVUS image. The next process is extracting plaque features to provide an accurate definition of the unstable (vulnerable) plaque. To achieve the above contribution, five algorithms are proposed to extract significant features from VH-IVUS images. Machine learning approaches are applied for training 440 in-vivo images obtained from 8 patients. Results proved the dominance of the proposed method for TCFA detection with accuracy rate of 98.02% compared with the 76.5% obtained by the cardiologist decision. Moreover, by validation of VH-IVUS images and their corresponding Optical Coherence Tomography (OCT) images, accuracy of 92.85% is achieved.

Trends in Applied Knowledge-Based Systems and Data Science

The Information Mining Engineering (IME) understands in processes, methodologies, tasks and techniques used to: organize, control and manage the task of finding knowledge patterns in information bases. A relevant task is selecting the data mining algorithms to use, which it is left to the expertise of the information mining engineer, developing it in a non-structured way. In this paper we propose an Information Mining Project Development Process Model (D-MoProPEI) which provides an integrated view in the selection of Information Mining Processes Based on Intelligent Systems (IMPbIS) within the Modeling Phase of the proposed Process Model through a Systematic Deriving Methodology.

Knowledge-Based Model of Expert Systems Using Rela-Model

Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundation...

A Recent Study on Hardware Accelerated Monte Carlo Modeling of Light Propagation in Biological Tissues

The Monte Carlo (MC) method is the gold standard in photon migration through 3D media with spatially varying optical proper-ties. MC offers excellent accuracy, easy-to-program and straightforward parallelization. In this study we summarize the recent advances in accelerating simulations of light propagation in biological tissues. The systematic literature review method is involved selecting the relevant studies for the research. With this approach research questions regarding the acceleration techniques are formulated and additional selection criteria are applied. The selected studies are analyzed and the research questions are answered. We discovered that there are several possibilities for accelerating the MC code and the CUDA platform is used in more than (60,)% of all studies. We also discovered that the trend in GPU acceleration with CUDA has continued in last two years.

Improved Stampede Prediction Model on Context-Awareness Framework Using Machine Learning Techniques

The determination of stampede occurrence through abnormal behaviors is an important research in context-awareness using individual activity recognition (IAR). An application such as an intelligent smartphone for crowd monitoring using inbuilt sensors is used. Meanwhile, there are few algorithms to recognize abnormal behaviors that can lead to a stampede for mitigation of crowd disasters. This study proposed an improved stampede prediction model which can facilitate abnormal detection with k-means. It can identify cluster areas among a group of people to know susceptible places that can help to predict stampede occurrence using IAR with the help of geographical positioning system (GPS) and accelerometer sensor data. To achieve this, two research questions were formulated and answered in this paper. (i) How to determine crowd of people in an area? (ii) How to know when stampede will occur in the identified area? The experimental results on the proposed model with decision tree (DT) algorithm shows an improved performance of 98.6 %, 97.7 % and 10.9 % over 94.4 %, 95 % and 18 % in the baselines for specificity, accuracy and false-negative rate (FNR) respectively thereby reducing high false negative alarm.

Determining of blood artefacts in endoscopic images using a software analysis

Wireless capsular endoscopy is a novel method of gastroenterology investigation during last years. Most important as well as the most difficult part of such investigation is the blood artefact determination. These artefacts are only be localised by medical experts trained in such field of gastroenterology medicine. This article describe the process of development a software solution which can be used to help localise some specific artefacts using developed algorithms. Such algorithms are firstly developed by Matlab solution while they are consequently transformed to developed software solution. Our solution was already preliminary tested by one of specific artefacts – blood artefacts, while the results have been found as sufficient to several use of this software. Future research will be focussed on other specific artefact description and algorithm development for detection.