Combined color and texture techniques provide new, useful features for blood cell analysis. After scene segmentation, texture line following programs define the borders of textons, the smallest texture grains. Size, color, and shape features are then calculated for each texton. The final feature data set includes: (1) The nucleus and cytoplasm features: size and shape, measurements of the texture lines from the green TV image, average CIE-PAL color coordinates and a color code. (2) The new texton features: total number of texton pixels with a specific color code, mean texton radius, size for each color code, and texton shape factors.

Combined local color and texture analysis of stained cells

General.- Towards an Image Analysis Center for Medicine.- 1. Introduction.- 2. The Interactive Image Analysis System.- 2.1 The Input Units.- 2.2 The Image Display System.- 2.3 The Computer System.- 3. The Computerized Microscope.- 3.1 The Input System.- 3.2 The Computer System.- 3.3 The Microscope Stage Controller.- 4. Discussion.- 5. Acknowledgements.- 6. References.- Cellular Computers and Biomedical Image Processing.- 1. Introduction.- 2. Cellular Computers.- 2.1 The von Neumann Cellular Automaton.- 2.2 Cellular Automata and Image Processing.- 2.3 Pipeline Image Processor.- 3. Cellular Computers and Image Processing Research.- 3.1 Programming the Cellular Computer.- 3.2 Research Status.- 4. Biomedical Applications of Cellular Digital Image Processing.- 4.1 Coronary Artery Disease.- 4.2 Tissue Growth and Development.- 4.3 Genetic Mutagen Studies.- 5. References.- Radiology.- Ultra High-Speed Reconstruction Processors for X-Ray Computed Tomography of the Heart and Circulation.- 1. Introduction.- 2. Computation and Display.- 2.1 The Cone Beam Geometry.- 2.2 Choice of a Suitable Reconstruction Algorithm.- 2.3 Filtration Methods.- 2.4 Reconstruction Processor Design.- 2.5 Back-Projection Implementations.- 3. Flexibility of the High Speed Parallel Processing Reconstruction Architecture.- 4. Acknowledgements.- 5. References.- Computer Analysis of the Ultrasonic Echocardiogram.- 1. Introduction.- 2. System Configuration.- 3. Processing of Ultrasonic Echocardiogram.- 4. Clinical Results.- 5. Conclusions.- 6. Acknowledgements.- 7. References.- Toward Computed Detection of Nodules in Chest Radiographs.- 1. Introduction.- 2. Materials and Methods.- 2.1 Preprocessor.- 2.2 Circularity Detector.- 2.3 Boundary Follower.- 2.4 Classifier.- 3. Experimental Results.- 4. Conclusions.- 5. Acknowledgements.- 6. References.- A Parallel Processing System for the Three-Dimensional Display of Serial Tomograms.- 1. Introduction.- 2. System Outline.- 2.1 Tomograms.- 2.2 Functions.- 3. System Architecture.- 4. Image Input Device.- 5. Image Processing.- 5.1 Reduction of Size.- 5.2 Smoothing.- 5.3 Binarization.- 5.4 Differentiation.- 5.5 Rotation.- 6. Three-Dimensional.- 7. Illustrative Experiment.- 8. Concluding Remarks.- 9. Acknowledgements.- 10. References.- Dynamic Computed Tomography for the Heart.- 1. Introduction.- 2. Dynamic Scanner.- 2.1 CT Images Using the Dynamic Scanner.- 2.2 ECG Gated Image Using the Dynamic Scanner.- 2.3 ECG Phase Differentiation Method.- 2.4 Comparison of Methods.- 3. Proposed Method for Faster Synchronization.- 3.1 Multilens Method.- 3.2 Rocking Fan Beam Method.- 4. Conclusion.- 5. References.- Efficient Analysis of Dynamic Images Using Plans.- 1. Introduction.- 2. Hardware System.- 3. Analysis of Heart Wall Motion in Cine-Angiograms.- 3.1 Plan-Guided Analysis of Thickness of the Heart Wall.- 3.2 Input Pictures.- 3.3 Planning for Feature Extraction.- 3.4 Efficient Heuristic Search for Smooth Boundaries.- 3.5 Selection of Frames for Analysis.- 3.6 Analysis of Consecutive Frames.- 3.7 Measurement of Wall Thickness.- 4. Conclusion.- 5. Acknowledgements.- 6. References.- Real-Time Image Processing in CT-Convolver and Back Projector.- 1. Introduction.- 2. Image Processing in CT.- 3. System Configuration.- 4. High Speed Processor.- 4.1 Convolution.- 4.2 Back Projection.- 4.3 Processing Time.- 5. Display System.- 6. References.- Histology and Cytology.- Detection of the Spherical Size Distribution of Hormone Secretory Granules from Electron Micrographs.- 1. Introduction.- 2. Materials and Methods.- 2.1 Manual Method for Size Distribution Analysis.- 2.2 Computer Method for Size Distribution Analysis.- 2.3 Analysis.- 3. Results.- 4. Discussion.- 5. Conclusion.- 6. References.- The Abbott Laboratories ADC-500T.M..- 1. Introduction.- 1.1 Rationale.- 1.2 Rate-Limiting Factors.- 2. Sample Preparation.- 2.1 The Spinner.- 2.2 The Stainer.- 3. Real-Time Blood Cell Image Analysis.- 3.1 The Computer-Controlled Microscope.- 3.2 Cell Acquisition.- 3.3 High Resolution Data Analysis.- 3.4 Cell Classification.- 3.5 System Timing.- 3.6 Review.- 4. Results.- 5. Summary and Conclusion.- 6. Future Trends.- 7. References.- An Approach to Automated Cytotoxicity Testing by Means of Digital Image Processing.- 1. Introduction.- 2. The Principle of the Lymphocytotoxicity Test.- 3. Description of Our Instrument.- 3.1 Image Input Device.- 3.2 Autofocusing Algorithm.- 3.3 Thresholding.- 3.4 Binary Pattern Matching.- 3.5 Cell Counting.- 4. Important Parameters to Determine Positivity.- 4.1 Cell Size.- 4.2 Density.- 4.3 Halo.- 5. Experimental Results.- 6. Summary.- 7. Acknowledgements.- 8. References.- The diff3T.M. Analyzer: A Parallel/Serial Golay Image Processor.- 1. Introduction.- 2. Functional Organization.- 3. Automated Microscope.- 3.1 Optics.- 3.2 Optical Bench.- 4. Golay Image Processor.- 4.1 Golay Logic Processor (GLOPR).- 4.2 Application of GLOPR.- 5. Summary.- 6. References.- Computer-Aided Tissue Stereology.- 1. Introduction.- 2. Analysis of Muscle Biopsies.- 3. Analysis of the Placenta.- 4. Analysis of Adipose Tissue.- 5. Computer-Aided Data Capture.- 6. Choice of Method.- 7. Acknowledgements.- 8. References.- Interactive System for Medical Image Processing.- 1. Introduction.- 2. Using the SUPRPIC Interactive Image Processing System.- 2.1 Cellular Logic for Image Processing.- 2.2 The Command Structure of SUPRPIC.- 3. Examples of Using SUPRPIC.- 3.1 Digitizing the Image.- 3.2 Determination of Tissue Image Architecture.- 4. Summary and Conclusions.- 5. References.- Real-Time Image Processing in Automated Cytology.- 1. Introduction.- 2. System Design and Specifications.- 2.1 System Design.- 2.2 System Specifications.- 3. Image Input.- 3.1 Image Input System Design.- 3.2 Image Sensor Module.- 3.3 Screening Module.- 3.4 Automated Focusing Module.- 4. Feature Extraction Method.- 4.1 Feature Extraction Hardware.- 4.2 Feature Extraction Procedure.- 5. Processing Sequence.- 6. Conclusion.- 7. References.- The Development of a New Model Cyto-Prescreener for Cervical Cancer.- 1. Introduction.- 2. Fundamental Ideas.- 2.1 Improvement of Diagnostic Performance.- 2.2 Improvement of Processing Speed.- 2.3 Automatic Slide Preparation.- 3. Improvement of Diagnostic Performance.- 3.1 Improvement of Cell Image Quality.- 3.2 Addition of Morphological Feature Parameters.- 3.3 Improvement of Preprocessing Program for Feature Extraction.- 3.4 Increase in Number of Cells Analyzed.- 4. Hardware Implementation.- 4.1 High Resolution TV Camera.- 4.2 Automatic Focusing Mechanism.- 4.3 High Speed Image Processor.- 4.4 Flexible Controllers.- 4.5 Automatic Smear Preparation.- 5. Conclusion.- 6. Acknowledgement.- 7. References.- Author Index.

Real-Time Medical Image Processing

As part of the diff3 System, the diff3 analyzer represents a practical application of real-time image processing to an important clinical need, automation of blood-smear analysis. To accomplish this task, the analyzer is supported by a complete complement of sample preparation reagents and instrumentation, including an Ames Company stainer and a proprietary diluter/spinner; design details for the latter are available [Maher and Pirc (1977a, 1977b, 1978)]. The sample preparation methodology has been evaluated by Nourbakhsh et al. (1978); it produces Wright’s stained blood smears, on standard microscope slides, which are of consistently high uniformity and quality (Color Plates 21 and 22).*

The diff3T.M. Analyzer: A Parallel/Serial Golay Image Processor

Some new color features and their application to cervical cell classification

Leukocyte image analysis in the diff3 system

Automated blood cell differentials using statistical classification techniques have been implemented in several commercial machines. The machine-derived features used to classify leukocytes resemble the descriptors used by humans performing visual classification, e.g. size, content, shape, color, and texture. However, because of our crude modeling of vision there is no universally accepted measure of characteristics such as shape, color, or texture. One expects, therefore, that features which are powerful discriminators for humans may perform poorly when quantified by machine, while other parameters may be more precisely measured automatically and so prove more useful in cell classification. This paper reports the results of a feature evaluation study for automated discrimination between all pairs of a large set of leukocyte classes consisting of both normal and abnormal types. To provide a framework for comparing automated feature ordering with the ranking attached by medical technologists, the machine-derived features were divided into six groups: size and content, mean and mode, cytoplasm/nucleus comparison, contrast and texture, color, and nuclear shape. A sequential procedure was used to select the best five-feature set from each group and the globally best five-feature set for each pairwise classification. The results illustrate the strengths and weaknesses of machine-derived descriptors for each type of decision.

Ashok V. Kulkarni

Papers

Effectiveness of feature groups for automated pairwise leukocyte class discrimination.