COMPUTATIONAL ANALYSIS OF THE HUMAN EYE WITH APPLICATIONS. için kapak resmi
COMPUTATIONAL ANALYSIS OF THE HUMAN EYE WITH APPLICATIONS.
Başlık:
COMPUTATIONAL ANALYSIS OF THE HUMAN EYE WITH APPLICATIONS.
Yazar:
Dua, Sumeet.
ISBN:
9789814340304
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 online resource (467 pages)
İçerik:
Contents -- 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 -- 2.2.1. Illumination Effect Reduction -- 2.2.1.1. 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 -- 2.3.2. Edge Linkage Methods for Boundary Detection -- 2.3.2.1. 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 -- 2.4.1.1. Geometric descriptors.

2.4.1.2. 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 -- 3.2. Economic Evaluation of the Prevention and Treatment of Vision-Related Diseases -- 3.2.1. 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 -- 3.4.1.2. Content-based image retrieval -- 3.4.2. Computational Decision Support System: Cataracts -- 3.4.2.1. Using classifiers -- 3.4.2.2. K nearest neighbors -- 3.4.2.3. GUI of the system -- 3.4.3. Computational Decision Support System: Glaucoma -- 3.4.3.1. Using fuzzy logic -- 3.4.3.2. Computational system using different classifiers -- 3.4.4. Computational Decision Support System: Blepharitis, Rosacea, Sjögren, and Dry Eyes -- 3.4.4.1. Utility of bleb imaging with anterior segment OCT in clinical decision making -- 3.4.4.2. Computational decision support system: RD -- 3.4.4.3. Role of computational system -- 3.4.5. Computational Decision Support System: Amblyopia.

3.4.5.1. Role of computational decision support system in amblyopia -- 3.5. Conclusion -- References -- Chapter 4. Hyperspectral Image Analysis for Oxygen Saturation Automated Localization of the Eye Bahram Khoobehi and James M. Beach -- 4.1. Introduction to Oxygen in the Retina -- 4.1.1. Microelectrode Methods -- 4.1.2. Phosphorescence Dye Method -- 4.1.3. Spectrographic Method -- 4.1.4. ThreeWavelength Method -- 4.1.5. DualWavelength Technique Using OD Ratio (ODR) -- 4.1.6. HSI Method -- 4.2. Experiment One -- 4.2.1. Methods and Materials -- 4.2.1.1. Animals -- 4.2.1.2. Systemic oxygen saturation -- 4.2.1.3. Intraocular pressure -- 4.2.1.4. Fundus camera -- 4.2.1.5. Hyperspectral imaging -- 4.2.1.6. Extraction of spectral curves -- 4.2.1.7. Mapping relative oxygen saturation -- 4.2.1.8. Relative saturation indices (RSIs) -- 4.2.2. Results -- 4.2.2.1. Spectral signatures -- 4.2.2.2. Oxygen breathing -- 4.2.2.3. Intraocular pressure -- 4.2.2.4. Responses to oxygen breathing -- 4.2.2.5. Responses to high IOP -- 4.2.3. Discussion -- 4.2.3.1. Pure oxygen breathing experiment -- 4.2.3.2. IOP perturbation experiment -- 4.2.3.3. Hyperspectral imaging -- 4.3. Experiment Two -- 4.3.1. Methods and Materials -- 4.3.1.1. Animals, anesthesia, blood pressure, and IOP perturbation -- 4.3.1.2. Hyperspectral recordings HSI was done as previously described. -- 4.3.1.3. Spectral determinant of percentage oxygen saturation -- 4.3.1.4. Spatial mapping of oxygen saturation: a modification of the previous mapping algorithm incorporating a correction for blood volume -- 4.3.1.5. Preparation and calibration of red blood cell suspensions -- 4.3.2. Results -- 4.3.2.1. Calibrated red-cell samples and retinal blood under controlled conditions -- 4.3.2.2. Oxygen saturation of the ONH -- 4.3.3. Discussion -- 4.3.4. Conclusions -- 4.4. Experiment Three.

4.4.1. Methods and Materials -- 4.4.1.1. Compliance testing -- 4.4.1.2. Hyperspectral imaging -- 4.4.1.3. Selection of ONH structures -- 4.4.1.4. Statistical methods -- 4.4.2. Results -- 4.4.2.1. Compliance testing -- 4.4.2.2. Blood spectra from ONH structures -- 4.4.2.3. Oxygen saturation of ONH structures -- 4.4.2.4. Oxygen saturation maps -- 4.4.3. Discussion -- 4.5. Experiment Four -- 4.5.1. Methods and Materials -- 4.5.2. Results -- 4.5.3. Discussion -- 4.6. Experiment Five -- 4.6.1. Methods and Materials -- 4.6.1.1. Modification of the fundus camera -- 4.6.1.2. Sodium fluorescein dye injection -- 4.6.1.3. Automatic control point detection -- 4.6.1.4. Fused image optimization -- 4.7. Conclusion -- References -- Chapter 5. Automated Localization of Eye and Cornea Using Snake and Target-Tracing Function Jen-Hong Tan, Ng, E.Y.K., Rajendra Acharya, U. and Chee, C. -- 5.1. Introduction to Thermography -- 5.2. Data Acquisition -- 5.3. Methods -- 5.3.1. Snake and GVF -- 5.3.2. Target Tracing Function and Genetic Algorithm -- 5.3.3. Locating Cornea -- 5.4. Results -- 5.5. Discussion -- 5.6. Conclusion -- References -- Chapter 6. Automatic Diagnosis of Glaucoma Using Digital Fundus Images Rajendra Acharya, U., Oliver Faust, Zhu Kuanyi,Tan Mei Xiu Irene, BooMaggie, Sumeet Dua, Tan Jen Hong and Ng, E.Y.K. -- 6.1. Introduction to Glaucoma -- 6.1.1. Glaucoma Types -- 6.1.1.1. Primary open-angle glaucoma -- 6.1.1.2. Angle-closure glaucoma -- 6.1.2. Diagnosis of Glaucoma -- 6.2. Materials and Methods -- 6.2.1. c/d Ratio -- 6.2.2. Measuring the Area of Blood Vessels -- 6.2.3. Measuring the ISNT Ratio -- 6.2.4. Classifier -- 6.3. Results -- 6.4. Discussion -- 6.5. Conclusion -- References -- Chapter 7. Temperature Distribution Inside the Human Eye with Tumor Growth Ooi, E.H. and Ng, E.Y.K. -- 7.1. Introduction to Temperature Distribution.

7.2. Classification of Eye Tumors -- 7.3. Mathematical Model -- 7.3.1. The Human Eye -- 7.3.2. The Eye Tumor -- 7.3.3. Governing Equations -- 7.3.4. Boundary Conditions -- 7.4. Material Properties -- 7.5. Numerical Scheme -- 7.5.1. Integro-Differential Equations -- 7.6. Results -- 7.6.1. Numerical Model -- 7.6.2. Case 1 -- 7.6.3. Case 2 -- 7.6.4. Discussion -- 7.7. Parametric Optimization -- 7.7.1. Analysis of Variance -- 7.7.2. Taguchi Method -- 7.7.3. Discussion -- 7.8. Concluding Remarks -- References -- Chapter 8. The Study of Ocular Surface Temperature by Infrared Thermography: The Principles, Methodologies, and Applications Jen-Hong Tan, Ng, E.Y.K., Rajendra Acharya, U. and Chee, C. -- 8.1. Introduction to IR Thermography -- 8.2. Infrared Thermography and the Measured OST -- 8.3. The Acquisition of OST -- 8.3.1. Manual Measures -- 8.3.2. Semi-Automated and Fully Automated -- 8.4. Applications to Ocular Studies -- 8.4.1. On Ocular Physiologies -- 8.4.2. On Ocular Diseases and Surgery -- 8.5. Discussion -- References -- Chapter 9. Automated Microaneurysm Detection in Fluorescein Angiograms for Diabetic Retinopathy Prerna Sethi and Hilary W. Thompson -- 9.1. Introduction -- 9.1.1. Preprocessing -- 9.1.1.1. Shade correction -- 9.1.1.2. Hough transform -- 9.1.1.3. Top-hat transform -- 9.1.2. Image Segmentation -- 9.1.2.1. The region approach -- 9.1.2.2. The gradient-based method -- 9.1.2.3. Edge detection -- 9.1.2.3.1. The first-order derivative (gradient) methods -- 9.1.2.3.2. The second-order derivative methods -- 9.1.2.3.3. The optimal edge detector -- 9.2. Image Registration -- 9.3. Classification -- 9.4. Automated, Integrated Image Analysis Systems -- 9.5. Conclusion -- References.

Chapter 10. Computer-Aided Diagnosis of Diabetic Retinopathy Stages Using Digital Fundus Images Rajendra Acharya, U., Oliver Faust, Sumeet Dua, Seah Jia Hong, Tan Swee Yang, Pui San Lai and Kityee Choo.
Özet:
Advances 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. This book is a novel and well-timed endeavor to present, in an amalgamated format, computational image modeling methods as applied to various extrinsic scientific problems in ophthalmology. It is self-contained and presents a highly comprehensive array of image modeling algorithms and methodologies relevant to ophthalmologic problems. The book is the first of its kind, bringing eye imaging and multi-dimensional hyperspectral imaging and data fusion of the human eye, into focus. The editors are at the top of their fields and bring a strong multidisciplinary synergy to this visionary volume. Their "inverted-pyramid" approach in presenting the content, and focus on core applications, will appeal to students and practitioners in the field.
Notlar:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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