Non-Linear Filters for Mammogram Enhancement: A Robust Computer-Aided Analysis Framework for Early Detection of Breast Cancer

ISBN-13: 9789811504419 / Angielski / Twarda / 2019 / 239 str.

Vikrant Bhateja; Mukul Misra; Shabana Urooj

Spis treści

Introduction: Computer-aided Analysis of Mammograms for Diagnosis of Breast Cancer.- Mammogram Enhancement: Background.- Methodology: Motivation, Objectives and Proposed Solution Approach.- Performance Evaluation and Benchmarking of Mammogram Enhancement Approaches: Mammographic Image Quality Assessment.- Non-linear Polynomial Filters: Overview, Evolution and Proposed Mathematical Formulation.- Non-linear Polynomial Filters for Contrast Enhancement of Mammograms.- Non-linear Polynomial Filters for Edge Enhancement of Mammograms.- Human Visual System Based Unsharp Masking for Enhancement of Mammograms.- Conclusions and Future Scope: Applications, Contributions and Impact.

O autorze

Vikrant Bhateja is an Associate Professor at the ECE Department, SRMGPC, Lucknow, where he also serves as the Head of Academics & Quality Control. He holds a doctorate in biomedical imaging and has 16 years of academic teaching experience, with over 120 publications in reputed international conference proceedings and journals to his credit. His areas of research include digital image and video processing, computer vision, medical imaging, and machine learning. Dr Vikrant has edited 20 books with Springer Nature. He is Editor-in-Chief of IGI Global—International Journal of Natural Computing and Research (IJNCR); an Associate Editor of the International Journal of Ambient Computing and Intelligence (IJACI); and a Guest Editor for journals including Evolutionary Intelligence and the Arabian Journal of Science and Engineering.

Mukul Misra is a Professor at the Faculty of ECE, Shri Ramswaroop Memorial University, India, where he has also served as the Director of Research since 2013. He received his PhD (Microwave Electronics) from Delhi University, India, in 2000; served as a Lecturer at Osaka University, Japan, in 2000; and was awarded a JSPS Post-doctoral Fellowship at the same university in 2001. He has served as a Research Scientist at the ILE, Osaka University, Japan; Research Officer at the University of Bath, UK; and a Facility Manager at the University of Warwick, UK. Dr Mukul has more than 30 publications in reputed international journals to his credit.

Dr Shabana Urooj is currently serving as HOD of Electrical Engineering at Gautam Buddha University, India. She received her Bachelor’s & Master’s degrees in Engineering from Aligarh Muslim University, India, and completed her doctorate in Biomedical Instrumentation at Jamia Millia Islamia, India. She has over 3 years of industrial and 17 years of teaching experience. She has authored more than 100 research papers in international journals and conference proceedings, and is an Associate Editor for several prominent journals, including IEEE Sensors, IEEE Transactions of Nanobiosciences, and IEEE Transactions of Nanotechnology. Presently, she is also Joint Secretary of the IEEE Delhi Section.

 

 

Blurb

This book presents non-linear image enhancement approaches to mammograms as a robust computer-aided analysis solution for the early detection of breast cancer, and provides a compendium of non-linear mammogram enhancement approaches: from the fundamentals to research challenges, practical implementations, validation, and advances in applications.

The book includes a comprehensive discussion on breast cancer, mammography, breast anomalies, and computer-aided analysis of mammograms. It also addresses fundamental concepts of mammogram enhancement and associated challenges, and features a detailed review of various state-of-the-art approaches to the enhancement of mammographic images and emerging research gaps. Given its scope, the book offers a valuable asset for radiologists and medical experts (oncologists), as mammogram visualization can enhance the precision of their diagnostic analyses; and for researchers and engineers, as the analysis of non-linear filters is one of the most challenging research domains in image processing.