Computer-Assisted Microscopy

1 Introduction. - The importance of images. - Why measure images?. - Computer methods: an overview. - Implementation. - Acquisition and processing of images. - Measurements within images. - More than two dimensions. - 2 Acquiring Images. - Image sources. - Multi-spectral images. - Image sensors. - Digitization. - Specifications. - References. - 3 Image Processing. - Point operations. - Time sequences. - Correcting image defects - averaging to reduce noise. - Reducing noise in a single image. - Frequency space. - Color images. - Shading correction. - Fitting backgrounds. - Rubber sheeting. - Image sharpening. - Focussing images. - References. - 4 Segmentation of Edges and Lines. - Defining a feature and its boundary. - Roberts' cross edge operator. - The Sobel and Kirsch operators. - Other edge-finding methods. - Other segmentation methods. - The Hough transform. - Touching features. - Manual outlining. - References. - 5 Discrimination and Thresholding. - Brightness thresholds. - Thresholding after processing. - Selecting threshold settings. - The need for automatic thresholding. - Automatic methods. - Histogram minimum method. - Minimum area sensitivity method 1ll. - Minimum perimeter sensitivity method. - Reproducibility testing. - Fixed percentage setting. - Color images. - Encoding binary images. - Contiguity. - References. - 6 Binary Image Editing. - Manual editing. - Combining images. - Neighbor operations. - Skeletonization. - Measurement using binary image editing. - Covariance. - Watershed segmentation. - Mosaic amalgamation and fractal dimensions. - Contiguity and filling interior holes. - References. - 7 Image Measurements. - Reference areas. - Boundary curvature. - Feature measurements. - Perimeter points. - Length and breadth. - Radius of curvature. - Image processing approaches. - Counting neighbor patterns. - Shape. - Cornersas a measure of shape. - Harmonic analysis. - Position. - Neighbor relationships. - Edge effects. - Brightness. - References. - 8 Stereological Interpretation of Measurement Data. - Global measurements. - Global parameters. - Mean free path. - Problems in 3-D interpretation. - Feature specific measurements. - Distribution histograms of size. - Interpreting distributions. - Nonparametric tests. - Cumulative plots. - Plotting shape and position data. - Other plots. - References. - 9 Object Recognition. - Locating features. - Parametric object description. - Distinguishing populations. - Decision points. - Other identification methods. - An example. - Comparing multiple populations. - An example of contextual learning. - Other applications. - Artificial intelligence. - References. - 10 Surface Image Measurements. - Depth cues. - Image contrast. - Shape from texture. - The scanning electron microscope. - Line width measurement. - Roughness and fractal dimensions. - Other surface measurement methods. - References. - 11 Stereoscopy. - Principles from human vision. - Measurement of elevation from parallax. - Presentation of the data. - Automatic fusion. - Stereoscopy in transparent volumes. - References. - 12 Serial Sections. - Obtaining serial section images. - Optical sectioning. - Presentation of 3-D image information. - Aligning slices. - Displays of outline images. - Surface modelling. - Measurements on surface-modelled objects. - Voxel displays. - Measurements on voxel images. - Network analysis. - Connectivity. - References. - 13 Tomography. - Reconstruction. - Instrumentation. - 3-D Imaging. - References. - 14 Lessons from Human Vision. - The language of structure. - Illusion. - Conclusion. - References. - For further reading. Language: English