Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

Textural Features for Image Classification

Foundations of Confocal Scanned Imaging in Light Microscopy -- Fundamental Limits in Confocal Microscopy -- Special Optical Elements -- Points, Pixels, and Gray Levels: Digitizing Image Data -- Laser Sources for Confocal Microscopy -- Non-Laser Light Sources for Three-Dimensional Microscopy -- Objective Lenses for Confocal Microscopy -- The Contrast Formation in Optical Microscopy -- The Intermediate Optical System of Laser-Scanning Confocal Microscopes -- Disk-Scanning Confocal Microscopy -- Measuring the Real Point Spread Function of High Numerical Aperture Microscope Objective Lenses -- Photon Detectors for Confocal Microscopy -- Structured Illumination Methods -- Visualization Systems for Multi-Dimensional Microscopy Images -- Automated Three-Dimensional Image Analysis Methods for Confocal Microscopy -- Fluorophores for Confocal Microscopy: Photophysics and Photochemistry -- Practical Considerations in the Selection and Application of Fluorescent Probes -- Guiding Principles of Specimen Preservation for Confocal Fluorescence Microscopy -- Confocal Microscopy of Living Cells -- Aberrations in Confocal and Multi-Photon Fluorescence Microscopy Induced by Refractive Index Mismatch -- Interaction of Light with Botanical Specimens -- Signal-to-Noise Ratio in Confocal Microscopes -- Comparison of Widefield/Deconvolution and Confocal Microscopy for Three-Dimensional Imaging -- Blind Deconvolution -- Image Enhancement by Deconvolution -- Fiber-Optics in Scanning Optical Microscopy -- Fluorescence Lifetime Imaging in Scanning Microscopy -- Multi-Photon Molecular Excitation in Laser-Scanning Microscopy -- Multifocal Multi-Photon Microscopy -- 4Pi Microscopy -- Nanoscale Resolution with Focused Light: Stimulated Emission Depletion and Other Reversible Saturable Optical Fluorescence Transitions Microscopy Concepts -- Mass Storage, Display, and Hard Copy -- Coherent Anti-Stokes Raman Scattering Microscopy -- Related Methods for Three-Dimensional Imaging -- Tutorial on Practical Confocal Microscopy and Use of the Confocal Test Specimen -- Practical Confocal Microscopy -- Selective Plane Illumination Microscopy -- Cell Damage During Multi-Photon Microscopy -- Photobleaching -- Nonlinear (Harmonic Generation) Optical Microscopy -- Imaging Brain Slices -- Fluorescent Ion Measurement -- Confocal and Multi-Photon Imaging of Living Embryos -- Imaging Plant Cells -- Practical Fluorescence Resonance Energy Transfer or Molecular Nanobioscopy of Living Cells -- Automated Confocal Imaging and High-Content Screening for Cytomics -- Automated Interpretation of Subcellular Location Patterns from Three-Dimensional Confocal Microscopy -- Display and Presentation Software -- When Light Microscope Resolution Is Not Enough:Correlational Light Microscopy and Electron Microscopy -- Databases for Two- and Three-Dimensional Microscopical Images in Biology -- Confocal Microscopy of Biofilms — Spatiotemporal Approaches -- Bibliography of Confocal Microscopy.

Handbook of biological confocal microscopy

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.

A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise

Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential

A survey on image segmentation

The advent of increased government involvement in occupational health maintenance with compensation for affected individuals is requiring new approaches to medical decision making. One aspect of this involvement will perhaps include the automatic mass diagnostic screening of medical films for the detection of a specffic abnormality with an occupational etiology. The results of two complementary approaches for performing diagnostic screening for presence and profusion of coal worker's pneumoconiosis from the routine posterior-anterior chest radiograph are presented. The first is a digital approach utilizing the measurement of image texture, while the second uses hybrid optical-digital methods involving the optical Fourier tramsform. Both approaches yielded classification results comparable to experienced radiologists.

Computer Diagnosis of Pneumoconiosis

Image processing by digital computer

A description of a prototype optical-digital system for automatic processing of chest X-rays and preliminary results are presented. The system consists of a digital image scanner used for locating the lung fields and texture measurements, a diffraction pattern sampling unit which provides both annular ring and wedge samples of the Fraunhofer diffraction pattern of lung areas, and a microcomputer for control and computation of decision functions. The film is positioned by a computer-controlled film transport to provide an overall automated system. The system is currently being designed for the Appalachian Laboratory for Occupational Respiratory Diseases and will be used for screening chest X-rays of coal miners for pneumoconiosis.

An Optical-Digital System For Automatic Processing Of Chest X-Rays

The results of two complementary approaches for performing diagnostic screening for the presence of coal workers' pneumoconiosis (CWP) from the routine posterior-anterior chest radiograph are presented. The first is a digital approach utilizing the measurement of image texture, while the second uses hybrid optical-digital methods involving the optical Fourier transform. Both approaches yield classification results comparable to experienced radiologists.

Automated computer screening of chest radiographs for pneumoconiosis.

A hybrid optical-digital system for automated measurement and disease classification of chest radiographs has been constructed. Initial results from the use of this system for classification of opacity textural patterns associated with a specific diffuse lung disease (coal workers pneumoconiosis) are presented. This system consists of three subsystems which include a digital image scanner for lung field detection, a computer-controlled X-Y transport for film positioning, and a Fraunhofer diffraction pattern sampling unit for opacity detection and measurement. A minicomputer is used to integrate these subsystems as well as render a film quality and disease classification through the use of discriminant functions and probability estimates.

Richard P. Kruger

Papers

Computer Diagnosis of Pneumoconiosis

Image processing by digital computer

An Optical-Digital System For Automatic Processing Of Chest X-Rays

Automated computer screening of chest radiographs for pneumoconiosis.

Hybrid optical-digital radiography-based system for lung disease detection.