Dermoscopy Image Analysis

M. Emre Celebi earned a B.Sc in computer engineering at the Middle East Technical University, Ankara, Turkey, in 2002. He earned M.Sc and Ph.D degrees in computer science and engineering at the University of Texas at Arlington, USA, in 2003 and 2006, respectively. A senior member of the IEEE and SPIE, he is currently an associate professor and the founding director of the Image Processing and Analysis Laboratory, Department of Computer Science, Louisiana State University, Shreveport, USA. Widely published, Dr. Celebi has worked on several projects funded by the U.S. National Science Foundation and National Institutes of Health. Teresa F. Mendonça earned mathematics and Ph.D degrees at the University of Porto, Portugal, in 1980 and 1993, respectively. Currently, she is an assistant professor with the Mathematics Department, Faculty of Sciences, University of Porto, and a researcher at the Institute for Systems and Robotics–Porto. Her research interests are in the areas of identification, modeling, and control applied to the biomedical field. In recent years, she has been involved in projects for modeling and control in anesthesia and in medical image analysis. Jorge S. Marques earned EE, Ph.D, and aggregation degrees at the Technical University of Lisbon, Portugal, in 1981, 1990, and 2002, respectively. Currently, he is an associate professor with the Electrical and Computer Engineering Department, Instituto Superior Técnico, Lisbon, Portugal, and a researcher at the Institute for Systems and Robotics, Portugal. He was the co-chairman of the IAPR Conference IbPRIA 2005, president of the Portuguese Association for Pattern Recognition (2001–2003), and associate editor of the Statistics and Computing Journal, Springer. His research interests are in the areas of statistical image processing, medical image analysis, and pattern recognition.

Toward a Robust Analysis of Dermoscopy Images Acquired under Different Conditions. A Bioinspired Color Representation for Dermoscopy Image Analysis. Where’s the Lesion? Variability in Human and Automated Segmentation of Dermoscopy Images of Melanocytic Skin Lesions. A State-of-the-Art Survey on Lesion Border Detection in Dermoscopy Images. Comparison of Image Processing Techniques for Reticular Pattern Recognition in Melanoma Detection. Global Pattern Classification in Dermoscopic Images. Streak Detection in Dermoscopic Color Images Using Localized Radial Flux of Principal Intensity Curvature. Dermoscopy Image Assessment Based on Perceptible Color Regions. Improved Skin Lesion Diagnostics for General Practice by Computer-Aided Diagnostics. Accurate and Scalable System for Automatic Detection of Malignant Melanoma. Early Detection of Melanoma in Dermoscopy of Skin Lesion Images by Computer Vision-Based System. From Dermoscopy to Mobile Teledermatology. PH2: A Public Database for the Analysis of Dermoscopic Images.