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Image Processing Fundamentals

Image Processing Fundamentals. Institute of Medical Engineering University of Lübeck Director: Prof. Dr. T. M. Buzug. Lecturer: Mandy Ahlborg. Image Representation – Continuous Model. Image Representation - Color Spaces. Image Representation - Color Spaces. - Cylindrical model.

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Image Processing Fundamentals

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  1. Image ProcessingFundamentals Institute of Medical Engineering University of Lübeck Director: Prof. Dr. T. M. Buzug Lecturer: Mandy Ahlborg

  2. Image Representation – Continuous Model

  3. Image Representation - Color Spaces

  4. Image Representation - Color Spaces - Cylindrical model - Additive color model - Subtractive color model - For printing (with K)

  5. Image Representation - RGB Red Green Blue

  6. Image Representation - HSV Hue Saturation Value

  7. Image Representation - Sampling Continuous Discrete

  8. Image Representation - Sampling 512 x 512 pixel 256 x 256 pixel 128 x 128 pixel 52 x 52 pixel

  9. Image Representation - Quantization 256 grey level 100 grey level 10 grey level 2 grey level

  10. Image Representation - Discrete Model sampled data continuous data sampled and quantized data discrete data

  11. Image Representation - Sampling & Quantization 512 x 512 pixel 256 grey level 256 x 256 pixel 100 grey level 128 x 128 pixel 10 grey level 52 x 52 pixel 2 grey level

  12. Image Representation - Grid Types Cell-centered grid Nodal grid

  13. Image Representation - Nyquist-Shannon-Theorem Nyquist-Shannon-Theorem fulfilled Nyquist-Shannon-Theorem not fulfilled

  14. Image Representation – Patient Coordinate System AXIAL: separates superior from inferior CORONAL:separates anterior from posterior SAGITTAL: separates anterior from posterior

  15. Relationships of Pixels - Neighbors 4-neighbors Diagonal neighbors 8-neighbors neighbor of

  16. Relationshipsof Pixels - Adjacency Let and pixels and be given. Which adjacencies hold in the following examples? 1 4 1 9 9 9 4 4-adjecent 8-adjecent m-adjacent 8-adjecent m-adjacent 8-adjecent (with not adjacent 5 8 8 9 1

  17. Relationshipsof Pixels - Path 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 no 4-path 8-path 8-path m-path

  18. Relationshipsof Pixels - DistanceMetrics Euclidean distance Manhatten distance Chebychev distance

  19. Relationshipsof Pixels - DistanceMetrics Eucledean distance Manhatten distance Chebychev distance

  20. Relationshipsof Pixels - DistanceMaps 1 3 2 2 1 1 1 4 3 2 1 2 1 2 2 1 1 1 1 4 3 2 1 1 2 1 1 1 1 3 2 1 1 1 1 2 1 1 1 1 2 1 1 2 1 1 1 1 1 2 1 2 1 1 2 3 1 1 2 2 2 1 1 2 3 4 1 1 1 1 2 3 3 2 2 1 1 3 4 5 Manhatten distance Chebyshev distance

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