04316nam 22006495 450 991048374060332120200701115009.0981-15-0442-310.1007/978-981-15-0442-6(CKB)4100000009845060(MiAaPQ)EBC6113340(DE-He213)978-981-15-0442-6(PPN)243769547(EXLCZ)99410000000984506020191102d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNon-Linear Filters for Mammogram Enhancement A Robust Computer-aided Analysis Framework for Early Detection of Breast Cancer /by Vikrant Bhateja, Mukul Misra, Shabana Urooj1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (xxviii, 239 pages) illustrationsStudies in Computational Intelligence,1860-949X ;861981-15-0441-5 Includes bibliographical references.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.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. .Studies in Computational Intelligence,1860-949X ;861Computational intelligenceOptical data processingRadiologyCancer researchComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Diagnostic Radiologyhttps://scigraph.springernature.com/ontologies/product-market-codes/H29013Cancer Researchhttps://scigraph.springernature.com/ontologies/product-market-codes/B11001Computational intelligence.Optical data processing.Radiology.Cancer research.Computational Intelligence.Image Processing and Computer Vision.Diagnostic Radiology.Cancer Research.618.1907572Bhateja Vikrantauthttp://id.loc.gov/vocabulary/relators/aut866314Misra Mukulauthttp://id.loc.gov/vocabulary/relators/autUrooj Shabanaauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910483740603321Non-Linear Filters for Mammogram Enhancement2073382UNINA