Professor, Electrical & Computer Engineering
Donadeo ICE 11-277
Mrinal K. Mandal received received his BE degree in Electronics and Communication Engineering from the National Institute of Technology, Durgapur, India in 1987; ME degree in Electronics and Communication Engineering from the Bengal Engineering and Science University, India in 1989; and MASc and PhD in Electrical and Computer Engineering from the University of Ottawa, Canada in 1995 and 1998, respectively.
From 1989 to 1992, he worked as a scientist in Indian Space Research Organization, Ahmedabad, India. From 1998 to 1999, he worked as a Software Developer at Corel Corporation, Ottawa. Since 1999 he has been with the Department of Electrical and Computer Engineering at the U of A, where he is currently a Professor. In 2005 he was a Humboldt Research Fellow at the Technical University of Berlin, Berlin, Germany.
Dr. Mandal is a member of several technical societies: SPIE, IEICE, a senior Member of IEEE, and a registered professional engineer in the province of Alberta, Canada. He is an editorial board member of ISRN Communications Journal and ICTACT Journal on Image and Video Processing. He is the author of the book Multimedia Signals and Systems (Kluwer Academic, 2003), and co-author of the book Continuous and Discrete Time Signals and Systems (Cambridge University Press, 2007). He has published over 130 papers in refereed journals and conferences, and holds a US patent on discrete wavelet transform architecture. He was the recipient of Canadian Commonwealth Fellowship from 1993 to 1998, and Humboldt Research Fellowship (Germany) from 2005-2006.
My research interests are in the area of image processing, with applications in medical imaging and multimedia. During the past one to two decades, there has been a significant increase in the level of interest in designing efficient image analysis systems for multimedia and medical applications. Many new application areas, such as the computer aided diagnostic systems, image visualization, content-based image and video retrieval, multimedia communications, image and video databases are feasible with the current technology. However, as new applications are coming up, new requirements are being specified, and a substantial amount of work remains to be accomplished in this area. My long term interest is in the development of image analysis techniques that help in computer aided diagnosis of selected critical diseases as well as in multimedia data analysis.
Over the past years my work focused on image and video compression, content-based image retrieval, and wavelet analysis. Currently, my research focus is primarily in medical imaging and super-resolution imaging.
- Computer-aided-diagnosis: We are developing efficient image analysis algorithms for healthcare applications. Three applications are being considered: (i) radiograph image analysis to detect TB; (ii) histopathological image analysis to detect melanoma (one type of skin cancer); and (iii) capsule endoscopic image analysis to detect bleeding in the gastro-intestinal tract. The developed techniques will be helpful in automated detection of critical diseases and also can be useful as a second opinion.
- Image analysis and feature detection using stochastic resonance: We are developing efficient image analysis techniques using stochastic resonance phenomenon. Both theoretical development of stochastic resonance and its application in signal and image processing are being considered. These techniques will be very useful for image analysis in a noisy environment. Two applications are considered: detection of lesions/tumors in MRI brain images (health application), and digital watermarking (consumer imaging application).
- Super resolution imaging and visualization: Here, our focus is on designing efficient low complexity techniques for generating high resolution videos by combining information from several low resolution videos. The developed techniques will be useful in areas such as medical data visualization, sports training, event modeling, and high definition video generation.