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Image Enhancement

Image Enhancement. T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar. Overview of part I. Subtraction imaging Gray-scale transforms Histogram transforms Global and local. Introduction, part I. Goal is to improve image quality

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Image Enhancement

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  1. Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar

  2. Overview of part I • Subtraction imaging • Gray-scale transforms • Histogram transforms • Global and local

  3. Introduction, part I • Goal is to improve image quality • One is sometimes forced to an ad hoc approach • Try several methods to see if they help • Result depends on the nature of the image and how well it matches with the assumptions of the enhancement method

  4. Subtraction imaging • Digital Subtraction Angiography (DSA) • Difference in images between before and after injecting contrast agent • Dual-energy and energy subtraction X-ray imaging • Hard and soft tissues absorb energy differently • Temporal subtraction

  5. Subtraction imaging, examples

  6. Gray-scale transforms • Thresholding • Binary images or limited intensity values • Gray-scale windowing • Use only a narrow band of intensity values • Gamma correction

  7. Gray-scale transforms, examples • Original CT image • Thresholded image, binary • Thresholded image, gray values preserved • Gray-scale windowed image

  8. Histogram transforms • Histogram equalization • Normalize the histogram to match uniform distribution • Implemented via a look-up table • Histogram specification • Use a prespecified spectrogram as a model • Global operations

  9. Histogram equalization, examples • Original image • Image after histogram equalization • Image after histogram equalization and windowing • Image after gamma correction (gamma = 0.3)

  10. Local-area and adaptive-neighborhood methods • Local-area histogram equalization (LAHE) • Histogram transformation is done in a moving-window with fixed size • Adaptive-neighborhood histogram equalization • Histogram transformation is done in a region with similar properties. • The region is grown from a seed pixel.

  11. Local-area and adaptive-neighborhood methods, examples • Original image • Histogram equalization • LAHE with 11 x 11 window • LAHE with 101 x 101 window • Adaptive neighborhood (growth tolerance 16, background width 5) • Adaptive neighborhood (growth tolerance 64, background width 8)

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