Abstract

Atherosclerotic plaque rupture is the most common mechanism responsible for the majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA) or “vulnerable plaque”. Virtual Histology Intravascular Ultrasound (VH-IVUS) image is clinically available for visualizing this colour coded coronary artery tissue. However, it has limitations in providing clinical relevant information for identifying the vulnerable plaque. In this talk, we discuss on the recent research on the detections of vulnerable plaque in virtual histology intravascular ultrasound images using machine learning algorithms. We proposed how to improve the identification of TCFA in VH-IVUS image by developing a set of algorithms for segmentation, feature extraction, and plaque type classification to accurately identify vulnerable plaque. To develop the algorithms two approaches comprising of optimization and semi-supervised models were adopted. Besides, K-means and Fuzzy c-means (FCM) were improved by Particle Swarm Optimization (KMPSO and FCMPSO). Next, semi-supervised models were developed by means of hybrid FCM with k-Nearest Neighbor (FCM-kNN), minimum Euclidean distance (FCM-mED), and Support Vector Machine (FCM-SVM). For the extraction, two algorithms were adopted: Close Lumen Tracing (CLBT) and Open Lumen Tracing (OLBT) to extract luminal features. In addition, three algorithms were explored for extracting significant features from plaque component consisting of Extracting Confluent Component (ECC), Necrotic Core Layering (NCL), and Plaque Burden Assessment (PBA). For plaque type classification, the extracted features from VH-IVUS were integrated with textural features to enhance the efficiency.

Biography

Prof. Ts. Dr. Ali Selamat  has received a B.Sc. (Hons.) in IT from Teesside University, U.K. and M.Sc. in Distributed Multimedia Interactive Systems from Lancaster University, U.K. in 1997 and 1998, respectively. He has received a Dr. Eng. degree from Osaka Prefecture University, Japan, in 2003. Ali Selamat is currently a professor at the School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM). He is presently serving as a Dean of Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia.Before that, he was a Chief Information Officer (CIO) and a Director of Communication and Information Technology Director, UTM. He is currently elected as a Chair of IEEE Computer Society, Malaysia Section under the Institute of Electrical and Electronics Engineers (IEEE), USA, and a Malaysia Engineering Deans Council member. He was previously assuming the position of research Dean on Knowledge Economy Research Alliance, UTM. He is currently elected as a fellow under Academy Professor Malaysia and a research fellow at Magicx - Media and Games Center of Excellence, Universiti Teknologi Malaysia. He was a principal consultant of Big Data Analytics, Ministry of Higher Education, Malaysia 201, and currently, a member of Malaysia Artificial Intelligence Roadmaps (2020-2021) and a keynote speaker in many international conferences. He was a visiting professor at Kuwait University and few other universities in Japan, Saudi Arabia, and Indonesia. Currently, he is a visiting professor at Hradec-Kralove University, Czech Republic, and Kagoshima Institute of Technology, Japan. Currently, he is serving as the Editorial Boards of International Journal of Knowledge-Based Systems Elsevier, Netherlands, International Journal of Information and Database Systems (IJIIDS) under Inderscience Publications, Switzerland, and Vietnam Journal of Computer Science under Springer Publications. He is the Program co-chair of IEA/AIE 2021: The 34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems in Kuala Lumpur, Malaysia. His research interests include data analytics, digital transformations, knowledge management in higher educations, key performance indicators, cloud-based software engineering, software agents, information retrievals, pattern recognition, genetic algorithms, neural networks, and soft-computing.