Computer-Assisted Microscopy: The Measurement and Analysis of Images

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