Hands-on Morphological Image Processing

by Edward R. Dougherty and Roberto A. Lotufo

SPIE PRESS Vol. TT59 * July 2003 290 pages * Softcover * 0-8194-4720-X

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Morphological image processing, now a standard part of the imaging scientist's toolbox, can be applied to a wide range of industrial applications. Concentrating on applications, this book shows how to analyze a problem and then develop successful algorithms based on the analysis. The book is hands-on in a very real sense: readers can download a demonstration toolbox of techniques and images from the web so they can process the images according to examples in the text.

Figures of Hands on Morphological Image Processing

Python scripts to generate most of the figures of the book. For MATLAB script see: handson

Chapter 1 - Binary Erosion and Dilation

Chapter 2 - Binary Opening and Closing

Chapter 3 - Morphological Processing of Binary Images

Chapter 4 - Hit-or-miss transform

  • Fig 4.1 Hit-or-miss transform
  • Fig 4.2 Recognition of noisy object
  • Fig 4.3 Object recognition using hit-or-miss
  • Fig 4.4 Sequential thinning with a single hit-or-miss template
  • Fig 4.5 Eight compass templates for sequential thinning
  • Fig 4.6 Sequential thinning using compass templates
  • Fig 4.7 Eight compass templates for sequential pruning
  • Fig 4.8 Thinning and pruning
  • Fig 4.9 Reconstruction of pruned skeleton
  • Fig 4.10 Detection of open connection in printed circuit
  • Fig 4.12 MT operators presented in this chapter

Chapter 5 - Gray-Scale Morphology

Chapter 6 - Morphological Processing of Gray-Scale Images

  • Fig 6.2 Morphological gradients
  • Fig 6.4 Gray-scale top-hat
  • Fig 6.5 Open top-hat to compensate uneven illumination
  • Fig 6.6 Detecting cells of a FISH image using open top-hat
  • Fig 6.8 Gray-scale alternating sequential filtering
  • Fig 6.10 Gray-scale morphological reconstruction
  • Fig 6.12 Gray-scale morphological reconstruction, marker at predefined location
  • Fig 6.13 Gray-scale morphological reconstruction by means of threshold decomposition
  • Fig 6.14 Gray-scale filling holes
  • Fig 6.15 Flat zones
  • Fig 6.16 Connected filter versus nonconnected filter
  • Fig 6.17 Reconstructive radial opening
  • Fig 6.18 Disjunctive and conjunctive reconstructive opening
  • Fig 6.20 Connected pyramid
  • Fig 6.21 Connected flat alternating sequential filtering
  • Fig 6.21 Regional maxima
  • Fig 6.26 Regional maxima of synthetic noise image
  • Fig 6.29 Airport runway detection
  • Fig 6.31 MT operators presented in this chapter

Chapter 7 - Morphological Processing of Gray-Scale Images

  • Fig 7.1 Segmentation by thresholded-gradient
  • Fig 7.2 Segmentation by watershed
  • Fig 7.4 Watershed oversegmentation
  • Fig 7.6 Watershed as a flooding process
  • Fig 7.8 Watershed segmentation
  • Fig 7.9 Voronoi diagram as the watershed of the distance transform
  • Fig 7.10 Geodesic SKIZ
  • Fig 7.11 Segmentation of overlapped convex cells
  • Fig 7.12 Segmentation of overlapped blood cells
  • Fig 7.13 Segmentation of keys using watershed transform with inner and outer markers
  • Fig 7.14 Segmentation of cornea cells from a noise image
  • Fig 7.17 Multiscale watershed
  • Fig 7.18 Dynamics versus number of most relevant regions
  • Fig 7.19 Image simplification
  • Fig 7.20 Watershed lines on a plateau
  • Fig 7.26 Silver-halide T-grain crystals analysis
  • Fig 7.27 MT operators presented in this chapter

Chapter 8 - Granulometries

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