Digital Processing of Biomedical Images

Until recently digital processing of biomedical images was conducted solely in the research laboratories of the universities and industry. However, with the advent of computerized tomography in 1972 and the computerized white blood cell differential count in 1974, enormous changes have suddenly occurred. Digital image pro­ cessing in biomedicine has now become the most active sector in the digital image processing field. Processing rates have reached the level of one trillion picture elements per year in the United States alone and are expected to be ten trillion per year in 1980. This enormous volume of activity has stimulated further re­ search in biomedical image processing in the last two years with the result that important inroads have been made in applications in radiology, oncology, and ophthalmology. Although much significant work in this field is taking place in Europe, it is in the United States and Japan that the level of activity is highest.

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.