LEADER 03918nam 22006495 450 001 9910483740603321 005 20251113205838.0 010 $a981-15-0442-3 024 7 $a10.1007/978-981-15-0442-6 035 $a(CKB)4100000009845060 035 $a(MiAaPQ)EBC6113340 035 $a(DE-He213)978-981-15-0442-6 035 $a(PPN)243769547 035 $a(EXLCZ)994100000009845060 100 $a20191102d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNon-Linear Filters for Mammogram Enhancement $eA Robust Computer-aided Analysis Framework for Early Detection of Breast Cancer /$fby Vikrant Bhateja, Mukul Misra, Shabana Urooj 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (xxviii, 239 pages) $cillustrations 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v861 311 08$a981-15-0441-5 320 $aIncludes bibliographical references. 327 $aIntroduction: 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. 330 $aThis book presents non-linear image enhancement approaches to mammograms as a robust computer-aided analysis solution for the early detection of breast cancer, and provides a compendium of non-linear mammogram enhancement approaches: from the fundamentals to research challenges, practical implementations, validation, and advances in applications. The book includes a comprehensive discussion on breast cancer, mammography, breast anomalies, and computer-aided analysis of mammograms. It also addresses fundamental concepts of mammogram enhancement and associated challenges, and features a detailed review of various state-of-the-art approaches to the enhancement of mammographic images and emerging research gaps. Given its scope, the book offers a valuable asset for radiologists and medical experts (oncologists), as mammogram visualization can enhance the precision of their diagnostic analyses; and for researchers and engineers, as the analysis of non-linear filters is one ofthe most challenging research domains in image processing. . 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v861 606 $aComputational intelligence 606 $aComputer vision 606 $aRadiology 606 $aCancer 606 $aComputational Intelligence 606 $aComputer Vision 606 $aRadiology 606 $aCancer Biology 615 0$aComputational intelligence. 615 0$aComputer vision. 615 0$aRadiology. 615 0$aCancer. 615 14$aComputational Intelligence. 615 24$aComputer Vision. 615 24$aRadiology. 615 24$aCancer Biology. 676 $a618.1907572 700 $aBhateja$b Vikrant$4aut$4http://id.loc.gov/vocabulary/relators/aut$0866314 702 $aMisra$b Mukul$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aUrooj$b Shabana$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483740603321 996 $aNon-Linear Filters for Mammogram Enhancement$92073382 997 $aUNINA