Digital Processing of Biomedical Images

Digital Image Processing in the United States.- 1. Introduction.- 2. Early Work in the United States.- 3. Developments in the 1960’s.- 4. Developments in the 1970’s.- 5. Conclusion.- 6. References.- Digital Image Processing in Japan.- 1. Introduction.- 2. Japan Society of Medical Electronics.- 3. Other Societies with Interests in Image Processing.- 4. Scope of Activity in Digital Processing of Biomedical Images.- 5. Image Data Base Exchange Between Japan and USA.- 6. References.- An Automated Microscope for Digital Image Processing — Part I: Hardware.- 1. Introduction.- 2. System Features.- 3. System Description.- 3.1 Optical and Mechanical Parts.- 3.2 Electronic Parts.- 4. Examples.- 5. Conclusion.- 6. Acknowledgments.- 7. References.- An Automated Microscope for Digital Image Processing — Part II: Software.- 1. Introduction.- 2. Program and Data Formats.- 3. Segment Programs.- 3.1 Controllers of a Microscope.- 3.2 Input Operation of Images.- 3.3 Store and Read of Images.- 3.4 Display of Images.- 3.5 Statistics of Gray Levels.- 3.6 Thresholding.- 3.7 Segmentation.- 3.8 Spatial Filtering (Mask).- 3.9 Arithmetic and Logical Operations Between Images.- 3.10 Geometric Transformations.- 3.11 Geometric Measurements.- 4. Conclusion.- 5. References.- Clinical Use of Automated Microscopes for Cell Analysis.- 1. Introduction.- 2. Hematology.- 3. Pattern Recognition.- 4. Commercial Clinical Systems.- 5. Future Expectations.- 6. References.- Multiband Microscanning Sensor.- 1. Introduction.- 2. General Description of the System.- 2.1 Hardware Construction.- 2.2 Functions of the System.- 3. Hardware System.- 3.1 System Configuration.- 3.2 Microspectrophotometer.- 3.2.1 Driving Mechanism for the Monochromator.- 3.2.2 Reference Beam Section.- 3.2.3 Measuring Beam Section.- 3.3 Stage Scanner.- 3.4 Disk-Type Point Scanner.- 3.5 Monitoring Display Unit.- 4. Results.- 5. References.- Computer Synthesis of High Resolution Electron Micrographs.- 1. Introduction.- 2. Synthetic Aperture.- 2.1 Synthetic Aperture Using a Conventional Electron Microscope.- 2.2 Synthetic Aperture Using the Scanning Transmission Electron Microscope.- 3. Cancer Virus Characterization.- 3.1 Automated Virus Search.- 3.2 High Resolution Studies.- 4. References.- Computer Processing of Electron Micrographs of DNA.- 1. Introduction.- 2. DNA Micrographs and Picture Processing Problems.- 3. Computer Extraction of DNA Strands.- 3.1 Preprocessing by Threshold Operation and Neighbor Operation.- 3.2 Noise Removal.- 3.3 Smoothing, Thinning, and Skeletonizing.- 4. Analysis of Line Patterns and DNA Strands.- 4.1 Connectivity Analysis.- 4.2 Line Segment Analysis.- 4.3 Segmentation of DNA Strands.- 4.4 Computation of the Length of DNA Strands.- 5. Concluding Remarks.- 6. References.- Significance Probability Mappings and Automated Interpretation of Complex Pictorial Scenes.- 1. Introduction.- 2. Image Analysis Tasks.- 3. Significance Probability Mapping.- 4. Image Representation as a Vector Field.- 5. Component Identification and Scene Encoding.- 6. Goal-Oriented System Approach.- 7. Acknowledgments.- 8. References.- Intracavitary Beta-Ray Scanner and Image Processing for Localization of Early Uterine Cancer.- 1. Introduction.- 2. Methods and Materials.- 2.1 Semiconductor Detector (SSD).- 2.2 Scanner.- 2.3 Measuring Circuits.- 2.4 Computer Data Processing.- 2.5 Effect of Collimation.- 3. Results.- 3.1 Comparison Between Computer Scan Map and Histopathological Map.- 3.2 Clinical Cases.- 4. Discussion.- 4.1 SSD Semiconductor Detector.- 4.2 Scanner.- 4.3 Safety.- 4.4 Uptake of 32p in the Tumor Tissue.- 4.5 Data Processing.- 4.6 Prospect in the Future.- 5. Conclusion.- 6. Acknowledgments.- 7. References.- New Vistas in Medical Reconstruction Imagery.- 1. Introduction.- 1.1 Characteristics of CT Imagery.- 1.2 Characteristics of Traditional Radiographic Imagery.- 1.3 The CT Brain Scanner.- 2. The Reconstruction Paradigm.- 3. Some Algorithms.- 3.1 Example.- 4. Impact on Medicine.- 5. Near Future Developments.- 6. Exemplary Projects.- 6.1 Data Base of Projection Data and Reconstruction Algorithms.- 6.2 General Purpose X-Ray Tomographic System.- 6.3 Nuclear Medicine Projects.- 7. Summary.- 8. References.- Digital Image Processing for Medical Diagnoses Using Gamma Radionuclides and Heavy Ions from Cyclotrons.- 1. Introduction.- 2. Nuclear Medicine Imaging.- 2.1 Hardware.- 2.2 Image Manipulation Software.- 2.3 Region-of-Interest Data Extraction.- 2.4 Time Gating.- 2.5 Subtraction Image.- 2.6 Clearance Rate Image.- 2.7 Transit Time Image.- 2.8 Rate of Uptake.- 2.9 T-max and N-max Images.- 2.10 Processing of Static Images.- 2.11 Three-dimensional Imaging Methods.- 2.12 Longitudinal Tomography.- 2.13 Longitudinal Tomography Using Fresnel Zone Plate.- 2.14 Transverse Section Tomography.- 3. Transverse Section Positron Annihilation Photon Imaging.- 4. Imaging with Heavy Ions.- 5. Summary.- 6. Acknowledgments.- 7. References.- Processing of RI-Angiocardiographic Images.- 1. Introduction.- 2. RI-Angiocardiography and Properties of RI-Angiocardiographic Images.- 3. Hardware for Image Processing.- 4. Extraction of the Left Ventricular Boundary.- 4.1 Boundary Detection by a Radial Scan Method.- 4.2 Boundary Tracing Using a Nonlinear Edge Detection Technique.- 5. Nonlinear Filter for Smoothing RI-Angiocardiographic Images.- 6. Concluding Remarks.- 7. Acknowledgments.- 8. References.- Bioimage Synthesis and Analysis from X-Ray, Gamma, Optical and Ultrasound Energy.- 1. Introduction.- 2. A Proposed Real-time X-Ray Reconstruction Instrument.- 2.1 The Dynamic Spatial Reconstructor (DSR).- 2.2 System Description of the DSR.- 3. Physiological Research with a Single Source Dynamic Spatial Reconstructor (SSDSR).- 3.1 Isolated Dead Canine Heart.- 3.2 Living Canine Thorax.- 3.3 Intact Living Canine Heart.- 4. Material Selective X-Ray Image Formation.- 5. Image Processing from Optically Derived Data.- 6. Determination of Tissue Form and Property by Ultrasound.- 7. An “Intelligent” High-Speed Computer Interface.- 8. Summary.- 9. Acknowledgments.- 10. References.- A Pap Smear Prescreening System: CYBEST.- 1. Introduction.- 2. Data Analysis and System Design.- 2.1 Feature Evaluation.- 3. Image Processing Techniques.- 4. The CYBEST System.- 4.1 Coarse Diagnosis.- 4.2 Fine Diagnosis.- 4.3 System Specifications.- 5. Result of Studies.- 6. References.- Automatic Analysis and Interpretation of Cell Micrographs.- 1. Introduction.- 2. Identification of Cells.- 3. Measurement of Cell Micrographs.- 4. Identification of Cell Micrographs by Elliptical Transformation.- 5.1 Normal Lymph Node.- 5.2 Nodular Lymphocytic Lymphoma.- 5.3 Hodgkin’s Granuloma.- 6. Acknowledgments.- 7. References.- Multi-Layer Tomography Based on Three Stationary X-Ray Images.- 1. Introduction.- 2. Method.- 2.1 Color Additive Identification of a Section.- 2.2 Digital Processing for the Enhancement of the Desired Section (I).- 2.2.1 Finding Mean Transmission.- 2.2.2 Identification of the Section with Allowance.- 2.3 Digital Processing for the Enhancement of the Desired Section (II).- 3. Results.- 3.1 Color Additive Analog Identification.- 3.2 Digital Coincidence Detection of Transmission.- 3.3 Enhancement of Tomosynthetic Section by Multiplication.- 4. Discussion and Conclusion.- 5. Acknowledgments.- Texture Analysis in Diagnostic Radiology.- 1. Introduction.- 1.1 Pulmonary Disease.- 1.2 Bone Disease.- 1.3 Computerized Axial Tomography.- 2. Some Automatic Texture Analysis Methods.- 2.1 Spatial Gray Level Dependence Method.- 2.2 Gray Level Run Length Method.- 3. An Interactive Texture Analysis Program.- 4. Texture Analysis Results.- 5. The Need for Image Manipulation Techniques for CT Data.- 5.1 Present Limitations.- 5.2 Three-Dimensional Display.- 5.3 Other Methods of Analysis.- 6. Examples of CT Clinical Image Processing.- 7. Acknowledgments.- 8. References.- Automated Diagnosis of the Congenital Dislocation of the Hip-Joint.- 1. Introduction.- 2. Quantitative Standards of LCC Diagnosis.- 2.1 Diagnostic Levels of LCC Specialists.- 2.2 Quantitative Standards.- 2.3 Comparison of Quantitative Diagnosis with the Diagnosis of Trained Specialists.- 3. The Computer Program for Automated Diagnosis of LCC.- 3.1 Limitation of Objective Regions.- 3.2 Extraction of the Contour of the Bone Edge.- 3.3 Simplification of Contour Lines.- 3.4 Curve Tracing of Hip-bone Borders.- 3.5 Extraction of Feature Points and Measurement of Parameters.- 4. Result and Conclusion.- 5. Acknowledgments.- 6. References.- Boundary Detection in Medical Radiographs.- 1. Introduction.- 2. Overview.- 3. The Lung Boundary.- 4. The Ribs.- 5. Lung Tumors.- 6. The Breast.- 7. Suspicious Regions.- 8. Concluding Remarks.- 9. Acknowledgments.- 10. References.- Feature Extraction and Quantitative Diagnosis of Gastric Roentgenograms.- 1. Introduction.- 2. Diagnosis of Gastric Roentgenograms.- 3. Recognition of the Gastric Contour.- 4. Interpretation of the Gastric Contour.- 4.1 Position Identification.- 4.2 Gastric Axis.- 4.3 Deviation Curve.- 4.4 Features.- 5. Conclusion.- 6. Acknowledgments.- 7. References.- Computer Processing of Chest X-Ray Images.- 1. Introduction.- 2. Preprocessing of Chest Radiographs.- 2.1 A Decision Function Method.- 2.2 Coarse Lung Boundary Extraction.- 2.3 Detailed Cardiac Boundary Extraction.- 2.4 Detailed Lung Boundary Detection.- 3. Rib Extraction in Chest X-Ray Photographs.- 4. Acknowledgments.- 5. References.- MINISCR-V2 — The Software System for Automated Interpretation of Chest Photofluorograms.- 1. Introduction.- 2. Construction of MINISCR-V2.- 3. Image Digitization and Reduction (Subsystem 0).- 4. Recognition of Borders of Lung Sections (Subsystem I).- 5. The Method for Recognition of Dorsal Portions of Ribs (Subsystem II).- 5.1 Filtering.- 5.2 Rough Estimation.- 5.3 Curve Fitting.- 5.4 Correction of Parameters.- 6. Detection of Abnormal Shadows in Lung (Subsystem III).- 6.1 Properties of Abnormal Shadows.- 6.2 Underlying Principles of the Method for Recognizing Abnormal Shadows.- 6.3 Procedure for Recognition of Abnormal Shadows in the Lung (I) — Stage 1. Rough Estimation.- 6.4 Procedure for Recognition of Abnormal Shadows in the Lung (II) — Stage 2. Close Examination of SR.- 7. Experimental Results.- 7.1 Recognition Success Rates.- 7.2 Memory and Time Requirements.- 8. Conclusion.- 8.1 The MINISCR-V2 System.- 8.2 The SLIP System.- 9. Acknowledgments.- 10. References.- Appendix 1. Computer Algorithms for Bridge Filter.- Appendix 2. Recognition of Ventral Portions of Ribs.- Automatic Recognition of Color Fundus Photographs.- 1. Introduction.- 2. Characteristics of Crossing Phenomena.- 3. Structure of Hardware.- 4. Structure of the Recognition Algorithm.- 5. Improvement of Picture Quality Using Color Information.- 6. Automatic Extraction of Blood Vessel Contour Lines.- 7. Classification of Crossing Phenomena.- 8. Conclusion.- 9. Acknowledgments.- 10. References.- Image Processing in Television Ophthalmoscopy.- 1. Introduction.- 2.1 Television and Optical System.- 2.1.1 Fundus Camera Modifications.- 2.1.2 35mm Slide System.- 2.1.3 Microscope System.- 2.1.4 Artificial Fundus.- 2.2 Light Source.- 2.2.1 Xenon Flash Source.- 2.2.2 D.C. Arc Source.- 2.2.3 Spectral Filters.- 2.2.4 Light Monitoring.- 2.3 Image Acquisition and Display System.- 2.3.1 Image Memory.- 2.3.2 Video Controller.- 2.4 The System Language (APL/EYE).- 2.4.1 Image Acquisition and Display.- 2.4.2 Image Processing.- 2.4.3 Graphics.- 3. Quantitative Retinal and Choroidal Angiography.- 3.1 Background.- 3.2 Clinical Applicability.- 3.3 Image Processing in Angiography.- 4. Fundus Reflectometry.- 4.1 Multispectral Sensing.- 4.2 Clinical Applicability.- 4.3 Radiative Transfer in the Fundus.- 4.4 Anatomical Stratification of Retinal Disorders.- 5. Oximetry.- 5.1 Background and Rationale.- 5.2 Clinical Applicability.- 5.3 Data-taking Procedures.- 6. Scene Analysis of the Ocular Fundus.- 6.1 Background and Rationale.- 6.2 Methodology for Disease Modeling.- 6.3 Clinical Significance.- 7. Health Care Significance.- 8. Acknowledgments.- 9. References.- Attendees.- Author Index.