Hybrid Image Processing Methods for Medical Image Examination

Venkatesan Rajinikanth is a Professor in Department of Electronics and Instrumentation Engineering at St. Joseph’s College of Engineering, Chennai 600119, Tamilnadu, India. Recently he edited a book titled ‘Advances in Artificial Intelligence Systems’, Nova Science publisher, USA. He has published more than 75 papers. He is the Associate Editor of Int. J. of Rough Sets and Data Analysis (IGI Global, US, DBLP, ACM dl) and Editing/Edited Special Issues in journals, such as Current Signal Transduction Therapy (Bentham Science), Current Medical Imaging Reviews (Bentham Science) and International Journal of Swarm Intelligence Research (IGI Global). He recently published an Indian patent titled ‘Disease Diagnosis System based on Electromyography’. His main research interests include Medical Imaging, Machine learning, and Computer Aided Diagnosis. Research Gate: https://www.researchgate.net/profile/Venkatesan_Rajinikanth E. Priya completed her Ph.D at MIT Campus, Anna University in the field of “Automated analysis using image processing and artificial intelligence for the diagnosis of tuberculosis images”. At present she is a Professor at the Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Affiliated to Anna University, Chennai. She has 17 years of teaching experience, 4 years of research experience and 3 years of industrial experience. She is a recipient of DST-PURSE fellow and a project participant of India-South African collaborative project titled “Development of computing tools for decision support in health assessment in rural areas”. Her areas of interest include bio-medical imaging, image processing, signal processing, application of artificial intelligence and machine learning techniques. She has published papers in refereed International Journals, Conferences and book chapters in the area of medical imaging and infectious diseases. Research Gate: https://www.researchgate.net/profile/Ebenezer_Priya Hong Lin holds a Ph.D. in Computer Science. His graduate work includes theoretical and empirical studies of parallel programming models and implementations. Dr. Lin has worked on large-scale computational biology at Purdue University, active networks at the National Research Council Canada, and network security at Nokia, Inc. Dr. Lin joined UHD at 2001 and he is currently a professor in computer science. He has worked on parallel computing, multi-agent systems, and affective computing since he joined UHD. He established the Grid Computing Lab at UHD through an NSF MRI grant. He has been a Scholars Academy mentor, an REU faculty mentor, and a CAHSI faculty mentor. His research interests include parallel/distributed computing, data analytics, and human-centered computing. He is a senior member of the ACM. Dr. Lin is the Chair of the inspiring conference series ‘Workshop on Computer Science and Engineering (WCSE)’. Research Gate: https://www.researchgate.net/profile/Hong_Lin5 Fuhua (Oscar) Lin is a Professor of School of Computing and Information Systems, Athabasca University, Canada. Further, Dr. Lin is also acting as guest professor in Waseda University (Japan) and China Jiliang University (China). Dr. Lin is a senior member in ACM and IEEE. He served as program committee member for various international conferences including the recent conferences, such as EduTrainment (2019), Canadian AI conference (2019), Intelligent Tutoring systems (2019) and General Executive Chair for the 4th IEEE CyberSciTech-2019. Further, he has received various prestigious awards including ‘Outstanding Leadership Award’ by IEEE in the year 2018. Dr. Lin has published more than 100 papers. His main research interests include Artificial Intelligence, Virtual Reality, e-Learning, Industrial Engineering, and Mathematical Modelling. Research Gate: https://www.researchgate.net/profile/Fuhua_Lin2

• 1. Introduction. 2. Image Examination. 3. Image Thresholding. 4. Image Segmentation. 5. Medical Image Processing with Hybrid Image Processing Method. 6. Deep Learning for Medical Image Processing. 7. Conclusion.