Prof. Hiroshi Fujita
Gifu University, Japan
Prof. Hiroshi Fujita received the B.S. and M.S. degrees in electrical engineering from Gifu University, Japan, in 1976 and 1978, respectively, and Ph.D. degree from Nagoya University in 1983. He became a research associate in 1978 and an associate professor in 1986 at Gifu National College of Technology. He was a visiting researcher at the K. Rossmann Radiologic Image Laboratory, University of Chicago, in 1983-1986. He became an associate professor in 1991 and a professor in 1995 in the Faculty of Engineering, Gifu University. He has been a professor and chair of intelligent image information since 2002 at the Graduate School of Medicine, Gifu University. He is now a Research Professor of Gifu University. He is a member of the Society for Medical Image Information (Honorary President), the Institute of Electronics, Information and Communication Engineers (Fellow), its Technical Groups on Medical Image (Adviser), the Japan Society for Medical Image Engineering (Director), and some other societies such as SPIE. He has been also served as scientific committee or program committee members, such as in International Workshop on Digital Mammography (Breast Imaging), SPIE Medical Imaging, and Computer Assisted Radiology and Surgery (CARS). He was worked as a General co-chair of Asian Forum on Medical Imaging 2007 held in Cheju National University, Korea, and as a General Chair of International Workshop for Breast Imaging (IWDM2014, Gifu). He has also worked as a Guest Editor-in-Chief in Special Section Editorial Committee for Medical Imaging, issued in April, 2013, from IEICE Society in Japan, and also as a Guest Editor-in-Chief in the Special Issue on Advanced Image Technologies in Diagnostic Imaging in 2018 in the Journal of Medical Imaging and Health Informatics. His research interests include computer-aided diagnosis system, image analysis and processing, and image evaluation in medicine. He has published over 1000 papers in Journals, Proceedings, Book chapters and Scientific Magazines.
Prof. M. Iqbal Saripan
Universiti Putra Malaysia, Malaysia
M. Iqbal Saripan is a professor in the area of digital image processing from Universiti Putra Malaysia. He completed his PhD from the University of Surrey, United Kingdom in 2006. He is currently the Deputy Vice Chancellor (Academics and Internatonal), Universiti Putra Malaysia since February 2017. He was the recipient of Top Research Scientist Malaysia (TRSM), 2013 National Young Scientist Award and 2012 The Young Outstanding Malaysian Award (TOYM). Recently in 2016, he has received an alumnus of the year for University of Surrey. He was listed as Top Ten Creative Young Malaysian by Top Ten Magazine in 2015. In January 2013, Elsevier awarded him as the Most Valued Reviewer for Radiation Physics and Chemistry Journal. Other selected awards in his list are; the Travel Bursary Award ISRP Melbourne (International Radiation Physics Society), twice the Best Paper Award in San Francisco and London, GOLD medal in Geneva, GOLD medal in PECIPTA, SILVER in MTE, UPM Excellent Young Researcher Award and UPM Excellent Consultant Award. Iqbal is currently a Chartered Engineer with Engineering Council UK since 2015. He is active in his learned societies in his area. Currently, he is the Vice Chairman of International Radiation Physics Society (IRPS) which members are the prominent figures in the area of radiation physics. Apart from that, he has also served Institute of Electrical and Electronics Engineers (IEEE) at various positions – chairman, vice chairman and executive committee members over the years. At national level, he is currently a treasurer of Young Scientist Network (YSN). His research area focusses on medical image processing, particularly in nuclear medical imaging of cells activities detection, which includes cancer and Alzheimer. He has published more than 100 journal papers in flagship & impact journal journals, such as IEEE Transactions on Nuclear Science, IEEE Transactions on Biomedical Engineering, Nuclear Instruments and Methods, and Radiation Physics and Chemistry. His total number of publications exceeds 250 papers. His research has been funded by more than RM10million from various national and international sources.
Prof. Mohd Shafry Mohd Rahim
Universiti Teknologi Malaysia, Malaysia
Mohd Shafry Mohd Rahim is currently a Professor and Chair, Office of Undergraduate Studies, Universiti Teknologi Malaysia (UTM). He is also a professor at School of Computing, UTM and also Research Fellow at Media and Game Innovation Centre of Excellence (MaGICX), Institute of. Human Centred Enggineering (iHumEn), UTM. Prof. Shafry has received a B.Sc. (Hons.) Computer Science and M.Sc. in Computer Science from Universiti Teknologi Malaysia (UTM), Malaysia in 1999 and 2002, respectively. He has received a PhD in Spatial Modelling from Universiti Putra Malaysia (UPM), Malaysia in 2008. His research interests in image processing, image data analytics, computer graphics and medical imaging.
Speech Title: "Medical Image Segmentation for Analystics"
Abstract: Humans have a strong ability to process millions of data and information to assist in the decision-making process. With new disruptive technology, trillion of data has been flooded into the cloud computing and require analytical process to produce valuable information. Images are one of the data collected using a variety of sensors that carry a lot of valuable information for the decision-making process. Therefore, Medical Image Analytics is a very significant research area to be strengthened in the new era of Big Data to improve healthcare industries with providing reliable information. The most important process in Medical Image Analytics is a image segmentation. Image segmentation is to extract clinically relevant information with intelligent insight. There are several method can be used in the segmentation process. In this discussion, several image segmentation method will be presented. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements also presented in the final section. In this keynote, the discussion also focus on experiences in Medical Image Analytics and discussing key challenges in various types of data for further research including semantic gaps.