Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies introduces the fundamentals and applications in the state of the art of rough fuzzy image analysis.
In the first chapter, the distinguished editors explain how fuzzy, near, and rough sets provide the basis for the stages of pictorial pattern recognition: image transformation, feature extraction, and classification. The text then discusses hybrid approaches that combine fuzzy sets and rough sets in image analysis, illustrates how to perform image analysis using only rough sets, and describes tolerance spaces and a perceptual systems approach to image analysis. It also presents a free, downloadable implementation of near sets using the Near Set Evaluation and Recognition (NEAR) system, which visualizes concepts from near set theory. In addition, the book covers an array of applications, particularly in medical imaging involving breast cancer diagnosis, laryngeal pathology diagnosis, and brain MR segmentation.
Edited by two leading researchers and with contributions from some of the best in the field, this volume fully reflects the diversity and richness of rough fuzzy image analysis. It deftly examines the underlying set theories as well as the diverse methods and applications.
Table of Contents
Cantor, Fuzzy, Near, and Rough Sets in Image Analysis, James F. Peters and Sankar K. Pal
Rough Fuzzy Clustering Algorithm for Segmentation of Brain MR Images, Pradipta Maji and Sankar K. Pal
Image Thresholding Using Generalized Rough Sets, Debashis Sen and Sankar K. Pal
Mathematical Morphology and Rough Sets, Homa Fashandi and James F. Peters
Rough Hybrid Scheme: An Application of Breast Cancer Imaging, Aboul Ella Hassanien, Hameed Al-Qaheri, and Ajith Abraham
Applications of Fuzzy Rule-Based Systems in Medical Image Understanding, Wojciech Tarnawski, Gerald Schaefer, Tomoharu Nakashima, and Lukasz Miroslaw
Near Set Evaluation and Recognition (NEAR) System, Christopher Henry
Perceptual Systems Approach to Measuring Image Resemblance, Amir H. Meghdadi and James F. Peters
From Tolerance Near Sets to Perceptual Image Analysis, Shabnam Shahfar, Amir H. Meghdadi, and James F. Peters
Image Segmentation: A Rough-Set Theoretic Approach, Milind M. Mushrif and Ajoy K. Ray
Rough Fuzzy Measures in Image Segmentation and Analysis, Dariusz Malyszko and Jaroslaw Stepaniuk
Discovering Image Similarities: Tolerance Near Set Approach, Sheela Ramanna
Sankar K. Pal is the director and a distinguished scientist of the Indian Statistical Institute in Kolkata.
James F. Peters is a professor in the Department of Electrical and Computer Engineering and group leader of the Computational Intelligence Laboratory at the University of Manitoba in Winnipeg, Canada.