Preface |
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xiii | |
Contributors |
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xvii | |
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1 | (26) |
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Why Automated Image Detection of Retinal Pathology? |
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1 | (6) |
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The general clinical need |
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2 | (1) |
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Diabetes: A global problem |
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2 | (1) |
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2 | (1) |
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Eye-screening for diabetic retinopathy |
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3 | (2) |
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Other retinal pathologies |
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5 | (1) |
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The retina as an indicator for disease elsewhere |
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6 | (1) |
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Research needs in automated retinopathy detection |
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6 | (1) |
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The engineering opportunity |
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7 | (1) |
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Automated Assessment of Retinal Eye Disease |
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7 | (6) |
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Automated microaneurysm detection in diabetic retinopathy |
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8 | (1) |
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9 | (1) |
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White lesion segmentation |
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9 | (1) |
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Localization of important markers |
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10 | (1) |
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Retinal vessel diameter changes in disease |
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11 | (1) |
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Retinal blood vessel segmentation |
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11 | (1) |
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Mathematical analysis of vessel patterns |
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12 | (1) |
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The Contribution of This Book |
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13 | (14) |
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Diabetic Retinopathy and Public Health |
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27 | (40) |
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27 | (1) |
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The Pandemic of Diabetes and Its Complications |
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28 | (1) |
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Retinal Structure and Function |
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29 | (6) |
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Definition and Description |
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35 | (5) |
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Classification of Diabetic Retinopathy |
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40 | (1) |
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Differential Diagnosis of Diabetic Retinopathy |
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40 | (2) |
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Systemic Associations of Diabetic Retinopathy |
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42 | (1) |
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42 | (1) |
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42 | (1) |
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42 | (1) |
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42 | (1) |
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43 | (1) |
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43 | (1) |
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43 | (1) |
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43 | (1) |
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43 | (1) |
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43 | (2) |
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43 | (1) |
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Hematological abnormalities |
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44 | (1) |
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Leukostasis and inflammation |
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44 | (1) |
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44 | (1) |
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45 | (1) |
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45 | (3) |
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Management of systemic associations |
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45 | (1) |
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45 | (1) |
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Investigational treatments |
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46 | (2) |
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48 | (7) |
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48 | (6) |
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54 | (1) |
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Cost effectiveness of screening |
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54 | (1) |
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Access to care and screening |
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54 | (1) |
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55 | (12) |
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Detecting Retinal Pathology Automatically with Special Emphasis on Diabetic Retinopathy |
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67 | (12) |
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67 | (1) |
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Approaches to Computer (Aided) Diagnosis |
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68 | (2) |
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Detection of Diabetic Retinopathy Lesions |
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70 | (1) |
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Detection of Lesions and Segmentation of Retinal Anatomy |
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71 | (1) |
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Detection and Staging of Diabetic Retinopathy: Pixel to Patient |
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71 | (1) |
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72 | (7) |
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Finding a Role for Computer-Aided Early Diagnosis of Diabetic Retinopathy |
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79 | (42) |
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Mass Examinations of Eyes in Diabetes |
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79 | (3) |
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Motive for accurate early diagnosis of retinopathy |
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80 | (1) |
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81 | (1) |
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Practical importance of the concept of screening |
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81 | (1) |
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Coverage and timely re-examination |
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81 | (1) |
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Developing and Defending a Risk Reduction Program |
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82 | (2) |
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Explaining why retinopathy is suitable for screening |
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82 | (1) |
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Understanding reasons for possible criticism |
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83 | (1) |
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Fulfilling criteria for screening tests |
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83 | (1) |
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Setting quality assurance standards |
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84 | (1) |
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84 | (1) |
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Assessing Accuracy of a Diagnostic Test |
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84 | (6) |
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Predictive value, estimation, power |
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85 | (2) |
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Receiver operating characteristic curve |
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87 | (2) |
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89 | (1) |
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90 | (1) |
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Improving Detection of Diabetic Retinopathy |
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90 | (3) |
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Improving work environment |
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91 | (1) |
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91 | (1) |
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91 | (1) |
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Avoiding loss of information |
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92 | (1) |
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92 | (1) |
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Ensuring accurate grading |
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93 | (1) |
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93 | (1) |
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Measuring Outcomes of Risk Reduction Programs |
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93 | (3) |
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Reducing new blindness and visual impairment |
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94 | (1) |
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Counting people who lost vision |
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94 | (1) |
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Understanding the importance of visual impairment |
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95 | (1) |
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User Experiences of Computer-Aided Diagnosis |
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96 | (7) |
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Perceived accuracy of lesion detection |
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97 | (4) |
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Finding and reading evaluations of software for retinopathy diagnosis |
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101 | (1) |
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Opportunities and challenges for programmers |
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102 | (1) |
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Planning a Study to Evaluate Accuracy |
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103 | (7) |
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Getting help from a statistician |
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103 | (1) |
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Choosing a measurement scale |
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103 | (1) |
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104 | (4) |
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Carrying out different phases of research |
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108 | (1) |
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An example from another field |
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109 | (1) |
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110 | (10) |
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Measures of Binary Test Performance |
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120 | (1) |
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Retinal Markers for Early Detection of Eye Disease |
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121 | (34) |
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121 | (1) |
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122 | (1) |
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Nonproliferative Diabetic Retinopathy |
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123 | (1) |
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124 | (4) |
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Related Works on Identification of Retinal Exudates and the Optic Disc |
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128 | (4) |
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Exudate identification and classification |
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128 | (2) |
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130 | (2) |
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132 | (2) |
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Pixel-Level Exudate Recognition |
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134 | (3) |
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Application of Pixel-Level Exudate Recognition on the Whole Retinal Image |
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137 | (2) |
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Locating the Optic Disc in Retinal Images |
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139 | (9) |
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141 | (1) |
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Color morphology preprocessing |
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141 | (3) |
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Accurate localization of the optic disc-based snakes |
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144 | (2) |
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Optic disc localization results |
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146 | (2) |
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148 | (7) |
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Automated Microaneurysm Detection for Screening |
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155 | (30) |
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Characteristics of Microaneurysms and Dot-Hemorrhages |
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155 | (1) |
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History of Automated Microaneurysm Detection |
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156 | (9) |
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Early morphological approaches |
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156 | (1) |
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The ``standard approach'' to automated microaneurysm detection |
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157 | (2) |
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Extensions of the standard approach |
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159 | (3) |
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162 | (2) |
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General red lesion detection |
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164 | (1) |
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Microaneurysm Detection in Color Retinal Images |
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165 | (2) |
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The Waikato Automated Microaneurysm Detector |
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167 | (5) |
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Further comments on the use of color |
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171 | (1) |
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Issues for Microaneurysm Detection |
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172 | (5) |
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172 | (1) |
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Image compression implications |
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173 | (2) |
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175 | (1) |
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Meaningful comparisons of implementations |
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175 | (2) |
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Research Application of Microaneurysm Detection |
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177 | (1) |
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178 | (7) |
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Retinal Vascular Changes as Biomarkers of Systemic Cardiovascular Diseases |
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185 | (36) |
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185 | (1) |
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Early Description of Retinal Vascular Changes |
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186 | (1) |
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187 | (2) |
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Assessment of retinal vascular signs from retinal photographs |
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187 | (1) |
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Limitations in current retinal vascular imaging techniques |
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187 | (2) |
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Retinal Vascular Changes and Cardiovascular Disease |
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189 | (5) |
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189 | (2) |
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Stroke and cerebrovascular disease |
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191 | (2) |
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Coronary heart disease and congestive heart failure |
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193 | (1) |
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Retinal Vascular Changes and Metabolic Diseases |
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194 | (3) |
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196 | (1) |
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196 | (1) |
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197 | (1) |
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Retinal Vascular Changes and Other Systemic Diseases |
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197 | (3) |
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197 | (1) |
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198 | (1) |
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Inflammation and endothelial dysfunction |
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198 | (2) |
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Subclinical cardiac morphology |
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200 | (1) |
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Genetic Associations of Retinal Vascular Changes |
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200 | (1) |
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201 | (1) |
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Retinal Vessel Caliber Grading Protocol |
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201 | (20) |
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202 | (2) |
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Example of the grading process |
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204 | (1) |
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205 | (1) |
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206 | (15) |
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Segmentation of Retinal Vasculature Using Wavelets and Supervised Classification: Theory and Implementation |
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221 | (48) |
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221 | (3) |
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224 | (11) |
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224 | (1) |
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225 | (3) |
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228 | (1) |
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Supervised classification |
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229 | (2) |
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231 | (1) |
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Bayesian Gaussian mixture model classifier |
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231 | (2) |
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k-nearest neighbor classifier |
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233 | (1) |
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Linear minimum squared error classifier |
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234 | (1) |
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Segmentation Using the 2-D Gabor Wavelet and Supervised Classification |
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235 | (10) |
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235 | (2) |
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2-D Gabor wavelet features |
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237 | (1) |
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238 | (1) |
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Supervised pixel classification |
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239 | (1) |
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240 | (1) |
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241 | (1) |
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242 | (3) |
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Implementation and Graphical User Interface |
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245 | (4) |
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245 | (1) |
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246 | (1) |
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246 | (1) |
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247 | (2) |
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249 | (9) |
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258 | (11) |
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258 | (1) |
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258 | (11) |
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Determining Retinal Vessel Widths and Detection of Width Changes |
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269 | (36) |
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Identifying Blood Vessels |
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270 | (1) |
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270 | (1) |
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Vessel Extraction Methods |
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271 | (1) |
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Can's Vessel Extraction Algorithm |
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271 | (5) |
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Improving Can's algorithm |
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272 | (3) |
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Limitations of the modified Can algorithm |
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275 | (1) |
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276 | (2) |
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Precise Boundary Detection |
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278 | (1) |
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Continuous Vessel Models with Spline-Based Ribbons |
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279 | (9) |
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Spline representation of vessels |
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279 | (5) |
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284 | (4) |
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Estimation of Vessel Boundaries Using Snakes |
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288 | (6) |
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288 | (1) |
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289 | (1) |
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289 | (3) |
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Cross section-based B-spline snakes |
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292 | (1) |
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B-spline ribbon snakes comparison |
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293 | (1) |
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Vessel Width Change Detection |
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294 | (4) |
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294 | (2) |
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Change detection via hypothesis test |
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296 | (2) |
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298 | (1) |
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298 | (7) |
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Geometrical and Topological Analysis of Vascular Branches from Fundus Retinal Images |
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305 | (34) |
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305 | (1) |
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Geometry of Vessel Segments and Bifurcations |
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306 | (6) |
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Arterial to venous diameter ratio |
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306 | (2) |
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308 | (3) |
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Vessel length to diameter ratios |
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311 | (1) |
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312 | (1) |
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Vessel Diameter Measurements from Retinal Images |
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312 | (3) |
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313 | (1) |
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314 | (1) |
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The sliding linear regression filter (SLRF) |
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314 | (1) |
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Clinical Findings from Retinal Vascular Geometry |
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315 | (3) |
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Topology of the Vascular Tree |
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318 | (5) |
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321 | (1) |
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321 | (1) |
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321 | (1) |
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322 | (1) |
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Automated Segmentation and Analysis of Retinal Fundus Images |
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323 | (5) |
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324 | (2) |
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326 | (1) |
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Analysis of binary images |
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327 | (1) |
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Clinical Findings from Retinal Vascular Topology |
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328 | (1) |
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329 | (10) |
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Tele-Diabetic Retinopathy Screening and Image-Based Clinical Decision Support |
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339 | (12) |
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339 | (1) |
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339 | (5) |
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340 | (1) |
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341 | (1) |
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342 | (1) |
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342 | (2) |
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Telemedicine Screening for Diabetic Retinopathy |
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344 | (2) |
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Image-Based Clinical Decision Support Systems |
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346 | (1) |
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347 | (4) |
Index |
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351 | |