LEADER 05815nam 2200685 a 450 001 9910437918303321 005 20200520144314.0 010 $a1-283-63443-0 010 $a9786613946881 010 $a94-007-5389-6 024 7 $a10.1007/978-94-007-5389-1 035 $a(CKB)2670000000256415 035 $a(EBL)1030695 035 $a(OCoLC)810931914 035 $a(SSID)ssj0000766962 035 $a(PQKBManifestationID)11511278 035 $a(PQKBTitleCode)TC0000766962 035 $a(PQKBWorkID)10732094 035 $a(PQKB)11235607 035 $a(DE-He213)978-94-007-5389-1 035 $a(MiAaPQ)EBC1030695 035 $a(PPN)168340763 035 $a(EXLCZ)992670000000256415 100 $a20120907d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aColor medical image analysis /$fM. Emre Celebi, Gerald Schaefer, editors 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (207 p.) 225 0$aLecture notes in computational vision and biomechanics,$x2212-9391 ;$vv. 6 300 $aDescription based upon print version of record. 311 $a94-017-8129-X 311 $a94-007-5388-8 320 $aIncludes bibliographical references and index. 327 $aColor Medical Image Analysis; Preface; Contents; A Data Driven Approach to Cervigram Image Analysis and Classification; 1 Introduction; 2 Related Work; 3 Methodology; 3.1 Visual Feature Extraction and Representation; Color Features; Texture Features; Image Similarity Measurement; 3.2 Finding the Region of Interest; 3.3 Cervigram Classification; 3.4 Support Vector Machine Classification; 3.5 Majority Vote Classification; 4 Results; 4.1 Isolating the Region of Interest; 4.2 Accuracy of Our Disease Classification; 4.3 Weighted Effect of Color and Texture; 5 Discussion and Conclusion; References 327 $aMacroscopic Pigmented Skin Lesion Segmentation and Its Influence on Lesion Classification and Diagnosis1 Introduction; 2 Pre-processing; 2.1 Shading Attenuation; 3 Segmentation; 3.1 Grayscale-Based Methods; 3.1.1 Thresholding-Based Methods; 3.1.2 Multi-Direction GVF Snake Method; 3.2 Multichannel-Based Methods; 3.2.1 Thresholding-Based Methods; 3.2.2 ICA-Based Active-Contours Method; 3.3 Comparison of Segmentation Methods Based on Experimental Results; 4 Feature Extraction for Skin Lesion Discrimination; 4.1 Features Used for Lesion Asymmetry Characterization 327 $a4.2 Features Used for Lesion Boundary Irregularity Characterization4.3 Features Used for Lesion Color Variation Characterization; 4.4 Features Used for Lesion Differential Structures Characterization; 4.5 Feature Extraction Summary; 5 Classification of Pigmented Skin Lesion Images; 5.1 Feature Normalization; 5.2 Defining Training Sets; 5.3 Classification Methods; 6 Discussion of Experimental Evidences: Pigmented Skin Lesion Segmentation and Its Influence on the Lesion Classification and Diagnosis; 7 Summary and Future Trends; References 327 $aColor and Spatial Features Integrated Normalized Distance for Density Based Border Detection in Dermoscopy Images1 Introduction; 2 Dermoscopy Image Analysis; 2.1 The ABCDE Rule; 3 Density Based Clustering; 3.1 DBSCAN; 3.2 Boundary Driven Density Clustering; 3.3 Expanding Cluster in FDBLD; 3.4 Selecting Leading Points; 3.5 FDBLD Algorithm; 4 Normalized Distance; 4.1 Effect of Color Spaces; 5 Experiments and Results on Dermoscopy Images; 6 Conclusion; References; A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions; 1 Introduction 327 $a2 Skin Class Hierarchy3 Hierarchical K-NN Classifier; 3.1 K-NN Classifier; 3.2 Learning Phase; 3.3 Classification Phase; 4 Feature Description; 4.1 Color Features; 4.2 Texture Features; 4.3 Ad Hoc Color Ratio Features; 4.4 Distance Measure; 5 Methods; 5.1 Acquisition and Preprocessing; 5.2 Highlight Removal; 5.3 Feature Normalization; 5.4 Evaluation; 5.4.1 Influence of the K Parameter; 5.4.2 Influence of Color Features; 5.4.3 Influence of Texture Features; 5.4.4 Influence of Feature Number and Selection Algorithm; 5.5 Comparison with Other Methods; 6 Overall Results; 7 Conclusions; Appendix 327 $aReferences 330 $aSince the early 20th century, medical imaging has been dominated by monochrome imaging modalities such as x-ray, computed tomography, ultrasound, and magnetic resonance imaging. As a result, color information has been overlooked in medical image analysis applications. Recently, various medical imaging modalities that involve color information have been introduced. These include cervicography, dermoscopy, fundus photography, gastrointestinal endoscopy, microscopy, and wound photography. However, in comparison to monochrome images, the analysis of color images is a relatively unexplored area. The multivariate nature of color image data presents new challenges for researchers and practitioners as the numerous methods developed for monochrome images are often not directly applicable to multichannel images. The goal of this volume is to summarize the state-of-the-art in the utilization of color information in medical image analysis. 410 0$aLecture Notes in Computational Vision and Biomechanics,$x2212-9391 ;$v6 606 $aDiagnostic imaging 606 $aImaging systems in medicine 615 0$aDiagnostic imaging. 615 0$aImaging systems in medicine. 676 $a621.36/7 701 $aCelebi$b M. Emre$01448701 701 $aSchaefer$b Gerald$01757512 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437918303321 996 $aColor medical image analysis$94195385 997 $aUNINA