Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen...
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Transcript of Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen...
![Page 1: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/1.jpg)
Image Enhancement
T-61.182, Biomedical Image AnalysisSeminar presentation 24.2.2005
Hannu LaaksonenVibhor Kumar
![Page 2: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/2.jpg)
Overview of part I
Subtraction imagingGray-scale transformsHistogram transforms Global and local
![Page 3: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/3.jpg)
Introduction, part I
Goal is to improve image qualityOne 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
![Page 4: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/4.jpg)
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
![Page 5: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/5.jpg)
Subtraction imaging, examples
![Page 6: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/6.jpg)
Gray-scale transforms
Thresholding Binary images or
limited intensity values
Gray-scale windowing Use only a narrow
band of intensity values
Gamma correction
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![Page 7: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/7.jpg)
Gray-scale transforms, examples
(a)Original CT image(b)Thresholded image,
binary(c)Thresholded image,
gray values preserved
(d)Gray-scale windowed image
![Page 8: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/8.jpg)
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
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![Page 9: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/9.jpg)
Histogram equalization, examples
(a) Original image(b) Image after histogram
equalization(c) Image after histogram
equalization and windowing
(d) Image after gamma correction (gamma = 0.3)
![Page 10: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/10.jpg)
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.
![Page 11: Image Enhancement T-61.182, Biomedical Image Analysis Seminar presentation 24.2.2005 Hannu Laaksonen Vibhor Kumar.](https://reader036.fdocuments.us/reader036/viewer/2022070409/56649e9e5503460f94ba0747/html5/thumbnails/11.jpg)
Local-area and adaptive-neighborhood methods, examples
(a) Original image(b) Histogram equalization(c) LAHE with 11 x 11
window(d) LAHE with 101 x 101
window(e) Adaptive neighborhood
(growth tolerance 16, background width 5)
(f) Adaptive neighborhood (growth tolerance 64, background width 8)