| Introduction |
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1 | (6) |
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What is Image Understanding Technology and why do we need it? |
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7 | (42) |
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Methods of Medical Image Acquisition |
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7 | (7) |
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Analysis and interpretation of medical images |
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14 | (2) |
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What new values can add to this scheme `automatic understanding'? |
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16 | (3) |
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Areas of applications for the automatic understanding of images |
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19 | (30) |
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T-formed area of applications for the automatic understanding of medical images |
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19 | (2) |
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The Automatic understanding of medical images as a tool for the preliminary classification of imaging screening data |
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21 | (8) |
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Automatic understanding in difficult medical problems |
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29 | (10) |
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Automatic understanding of images as a tool for semantic searching in data bases and successful web crawling |
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39 | (10) |
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A General Description of the Fundamental Ideas Behind Automatic Image Understanding |
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49 | (14) |
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49 | (3) |
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What does image understanding mean? |
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52 | (4) |
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Linguistic description of images |
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56 | (4) |
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The use of graph grammar to cognitive resonance |
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60 | (3) |
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Formal Bases for the Semantic Approach to Medical Image Processing Leading to Image Understanding Technology |
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63 | (18) |
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Fundamentals of syntactic pattern recognition methods |
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63 | (13) |
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Definitions and basic formalisms associated with syntactic pattern recognition methods |
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63 | (8) |
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Principles of syntax analysers operation |
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71 | (5) |
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Characteristic features and advantages of structural approaches to medical image semantic analysis |
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76 | (5) |
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Examples of Structural Pattern Analysis and Medical Image Understanding Application to Medical Diagnosis |
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81 | (60) |
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81 | (3) |
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Pre-processing Methods Designed to Process Selected Medical Images |
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84 | (21) |
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A Need to Apply Medical Data Pre-processing |
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84 | (2) |
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Recommended Stages of Medical Data Pre-processing |
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86 | (1) |
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Segmentation and Filtering of Images |
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87 | (4) |
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Skeletonisation of the Analysed Anatomical Structures |
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91 | (1) |
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Analysis of Skeleton Ramifications |
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92 | (4) |
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Smoothing skeletons of the analysed anatomical structures |
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96 | (1) |
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Transformation Straightening the External Contours of Analysed Objects |
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97 | (2) |
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Straightening Transformation Algorithm |
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99 | (5) |
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Basic Advantages of the Proposed Pre-processing Method |
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104 | (1) |
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Making Lexical Elements for the Syntactic Descriptions of Examined structures |
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105 | (3) |
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Structural Analysis of Coronary Vessels |
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108 | (8) |
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Syntactic Analysis and Diagnosing Coronary Artery Stenoses |
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109 | (2) |
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Recognition Results Obtained with the Use of Context-free Grammar |
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111 | (4) |
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115 | (1) |
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Structural Analysis and Understanding of Lesions in Urinary Tract |
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116 | (6) |
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Diagnosing Stenosis of the Ureter Lumen |
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117 | (1) |
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Application of Graph Grammar in the Analysis of Renal Pelvis Shape |
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118 | (4) |
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Syntactic Methods Supporting the Diagnosis of Pancreatitis and Pancreas Neoplasm |
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122 | (9) |
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Context-free Grammar in the Analysis of Shapes of Pancreatic Ducts |
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124 | (2) |
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Languages of Shape Feature Description in the Analysis of Pancreatic Duct Morphology |
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126 | (2) |
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Results of Syntactic Method Analysis of Pancreatic Ducts |
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128 | (3) |
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Analyses of MR images of spinal cord |
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131 | (3) |
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134 | (1) |
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135 | (6) |
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The application of the Image Understanding Technology to Semantic Organisation and Content-Based Searching in Multimedia Medical Data Bases |
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141 | (6) |
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Strengths and Weaknesses of the Image Understanding Technology Compared to Previously Known Approaches |
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147 | (4) |
| References |
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151 | |
