LEADER 05353nam 2200649 a 450 001 9910464514903321 005 20200520144314.0 010 $a1-283-23493-9 010 $a9786613234933 010 $a981-4340-30-8 035 $a(CKB)3400000000016632 035 $a(EBL)840602 035 $a(OCoLC)748215475 035 $a(SSID)ssj0000535284 035 $a(PQKBManifestationID)12215981 035 $a(PQKBTitleCode)TC0000535284 035 $a(PQKBWorkID)10523177 035 $a(PQKB)10277537 035 $a(MiAaPQ)EBC840602 035 $a(WSP)00008074 035 $a(Au-PeEL)EBL840602 035 $a(CaPaEBR)ebr10493530 035 $a(CaONFJC)MIL323493 035 $a(EXLCZ)993400000000016632 100 $a20110929d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aComputational analysis of the human eye with applications$b[electronic resource] /$fSumeet Dua, Rajendra Acharya U., E.Y.K. Ng, editors 210 $aHackensack, N.J. $cWorld Scientific$d2011 215 $a1 online resource (467 p.) 300 $aDescription based upon print version of record. 311 $a981-4340-29-4 320 $aIncludes bibliographical references and index. 327 $aContents; Chapter 1. The Biological and Computational Bases of Vision Hilary W. Thompson; 1.1. Introduction to the Eye; 1.2. The Anatomy of the Human Visual System; 1.3. Neurons; 1.4. Synapses; 1.5. Vision - Sensory Transduction; 1.6. Retinal Processing; 1.7. Visual Processing in the Brain; 1.8. Biological Vision and Computer Vision Algorithms; References; Chapter 2. Computational Methods for Feature Detection in Optical Images Michael Dessauer and Sumeet Dua; 2.1. Introduction to Computational Methods for Feature Detection; 2.2. Preprocessing Methods for Retinal Images 327 $a2.2.1. Illumination Effect Reduction2.2.1.1. Non-linear brightness transform; 2.2.1.2. Background identification methods; 2.2.2. Image Normalization and Enhancement; 2.2.2.1. Color channel transformations; 2.2.2.2. Image smoothing through spatial filtering; 2.2.2.3. Local adaptive contrast enhancement; 2.2.2.4. Histogram transformations; 2.3. Segmentation Methods for Retinal Anatomy Detection and Localization; 2.3.1. A Boundary Detection Methods; 2.3.1.1. First-order difference operators; 2.3.1.2. Second-order boundary detection; 2.3.1.3. Canny edge detection 327 $a2.3.2. Edge Linkage Methods for Boundary Detection2.3.2.1. Local neighborhood gradient thresholding; 2.3.2.2. Morphological operations for edge link enhancement; 2.3.2.3. Hough transform for edge linking; 2.3.3. Thresholding for Image Segmentation; 2.3.3.1. Segmentation with a single threshold; 2.3.3.2. Multi-level thresholding; 2.3.3.3. Windowed thresholding; 2.3.4. Region-Based Methods for Image Segmentation; 2.3.4.1. Region growing; 2.3.4.2. Watershed segmentation; 2.3.4.3. Matched filter segmentation; 2.4. Feature Representation Methods for Classification; 2.4.1. Statistical Features 327 $a2.4.1.1. Geometric descriptors2.4.1.2. Texture features; 2.4.1.3. Invariant moments; 2.4.2. Data Transformations; 2.4.2.1. Fourier descriptors; 2.4.2.2. Principal component analysis (PCA); 2.4.3. Multiscale Features; 2.4.3.1. Wavelet transform; 2.4.3.2. Scale-space methods for feature extraction; 2.5. Summary; References; Chapter 3. Computational Decision Support Systems and Diagnostic Tools in Ophthalmology: A Schematic Survey Sumeet Dua and Mohit Jain; 3.1. Evidence- and Value-Based Medicine; 3.1.1. EBM Process; 3.1.2. Evidence-Based Medical Issues; 3.1.3. Value-Based Evidence 327 $a3.2. Economic Evaluation of the Prevention and Treatment of Vision-Related Diseases3.2.1. Economic Evaluation; 3.2.2. Decision Analysis Method; 3.2.3. Advantages of Decision Analysis; 3.2.4. Perspective in Decision Analysis; 3.2.5. Decision Tree in Decision Analysis; 3.3. Use of Information Technologies for Diagnosis in Ophthalmology; 3.3.1. Data Mining in Ophthalmology; 3.3.2. Graphical User Interface; 3.4. Role of Computational System in Curing Disease of an Eye; 3.4.1. Computational Decision Support System: Diabetic Retinopathy; 3.4.1.1. Wavelet-based neural network23 327 $a3.4.1.2. Content-based image retrieval 330 $aAdvances in semi-automated high-throughput image data collection routines, coupled with a decline in storage costs and an increase in high-performance computing solutions have led to an exponential surge in data collected by biomedical scientists and medical practitioners. Interpreting this raw data is a challenging task, and nowhere is this more evident than in the field of opthalmology. The sheer speed at which data on cataracts, diabetic retinopathy, glaucoma and other eye disorders are collected, makes it impossible for the human observer to directly monitor subtle, yet critical details. T 606 $aEye 608 $aElectronic books. 615 0$aEye. 676 $a617.700285 701 $aDua$b Sumeet$0894685 701 $aAcharya U$b Rajendra$0731140 701 $aNg$b E. Y. K$0894686 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464514903321 996 $aComputational analysis of the human eye with applications$91998631 997 $aUNINA