1. Antti Tuomas Jalava Jaime Garrido Ceca
Image enhancement
Antti Tuomas Jalava
Jaime Garrido Ceca

2. Overview
Digital subtraction angiography. Dual-energy and energy-subtraction X-ray imaging. Temporal subtraction.
Gray-scale transform.
Convolution mask operators.
High-frequency enhancement.
Adaptive contrast enhancement.
Objective assessment of Contrast Enhancement.

3. Digital Subtraction Angiography
PROCESS :
Agent is injected to increase the density of the blood
Number of X-ray images.
An image taken before the injection of the agent is used as the mask or reference image.
Subtracted from the “live” images to obtain enhanced images.
Useful to detect sclerosis.
The mathematical procedure involved may be expressed simply as:
Sensitive to motion

5. Dual-energy and Energy-subtraction X-ray Imaging
X-ray images at multiple energy levels
Distribution of specific materials in the object or body imaged
Weighted combinations of multiple-energy images
soft-tissue and hard tissue separately

7. Temporal Subtraction
To detect normal or pathological changes occurred over a period of time.
Detection of lung nodules
Normal anatomic structures are suppressed and pathological are enhanced.

8. Gray Scale Transform Overview
Gray-scale thresholding.
Gray-scale windowing.
Gamma correction.
Histogram transformation.
Histogram specification.
Limitation of global operations.
Local-area histogram equalization.
Adaptive-neighborhood histogram equalization.

9. Gray-scale Transforms (I)
Presence of different levels of density or intensity in the image.
Histogram gray-scale transform.
Improve the visibility of details.

10. Gray-scale Transforms (II)
Gray-scale thresholding.
Gray level object > L new bi-level image.
Problem: Narrow range of gray levels.
Solution: Stretch the range of interest to the full range.
Gray-scale windowing.
Linear transformation
Gamma correction.
Non-linear transformations

11. Thresholding
Original image
New Image
L = 30

12. Gamma Curve

13. Gamma Correction
Original image
New image
γ = 0.3

14. Windowing
Original image
New image
f1 = f2 = 60

15. Histogram Transformation
Principle: maximal information is conveyed when PDF is uniform.
Histogram transformation is used to enhance the image.
Histogram-based methods:
Histogram equalization.
Histogram specification.
Local-area histogram equalization (LAHE).
Adaptive-neighborhood histogram equalization.

16. Histogram Equalization
Goal:
Discrete version:
Properties of this function:
Single value monotonically increasing.
Maintain same range of values.

17. Original image
Equalized image

18. Histogram of the original image
Equalized Histogram

19. Histogram Specification
Problem: H. Equalization provides only one output image. Not satisfactory in many cases.
Histogram Specification is a series of histogram-equalization steps to obtain prespecified histogram.
Process:
Specify the desired histogram and derive
Derive the histogram-equalizing transform
Derive from
Obtain
Transform to image f.

20. Limitations of Global Operations
Global operators (Gray-scale & histogram transform) provides simple mechanisms to manipulate the image.
Global approach to image enhancement ignores the nonstationary nature of images.
Given wide range of details of interest in medical image, such as hard and soft tissues, it is desirable to design local and adaptive transform for effective image enhancement.

21. Local-area Histogram Equalization (LAHE)
Problem: Gray levels with low probability are merged upon quantization of the equalizing transform lost in the enhanced image.
2D sliding window.
Resulting transform is applied only to the central pixel.
Computationally expensive.
LAHE variation:
Not every pixel. Only nonoverlapping rectangular block spanning the image.

22. Adaptive-neighborhood Histogram Equalization
Limitation of LAHE: no justification to the choice of the rectangular shape and the size of the window.
Identification of shape and size neighborhoods for each pixel by region growing.
Uniform region spans a limited range of gray levels by a specified threshold.
Local area composed not only by foreground region growing but also by background one.
Histogram of the local region equalizing transform to the seed pixel and all the pixels with the same value.

23. Adaptive-neighborhood Histogram Equalization
Original
Equalization,
Background depth 5,
growth threshold 16

24. Convolution Mask Operators for Image Enhancement
2D convolution of images with 3 x 3 masks.
Unsharp masking
Subtraction Laplacian

25. Convolution Mask Operators Unsharp Masking
Tackles blurring by an unknown phenomenon.
Assumes that each pixel of original image contributes also to neighboring pixels.
Results into a fog.
Procedure:
The original degraded image is blurred.
The blurred image is subtracted from the degraded image.
Removes the fog.
General form:
Where is local mean in degraded image
Mean filter
Unsharp mask

26. Convolution Mask Operators Subtraction Laplacian
Assumption that degraded image is a result of diffusion process that spreads intensity values over space as a function of time
3 x 3 convolution mask form
of Laplacian (gradient):
With weighting factor set to 1
the subtraction Laplacian is:

27. Convolution Mask Operators Problems
Edge enhancement & high-frequency emphasis (Over and under-shoot seen as halos around edges).
While seeming sharper, some finer details might be lost.
Can lead to negative pixel values.
Linear mapping back to display range can cancel any enhancing.
Fixed operators.
No adaptivity to variability within image.

28. Normalized dynamic range
Original
Unsharp mask,
A = 1, B = 9,
Normalized dynamic range
Unsharp mask,
A = 1, B = 9, Dynamic range cut to original
Subtracting Laplacian,
A = 1, B = 5,
Normalized dynamic range

29. High-frequency Emphasis
Bad idea: Ideal highpass filter
Introduces ringing artifacts.
Butterworth highpass filter
Use of smooth transition from stopband to pass band.
Artifact reduction.
Extracts only edges.
Order n.
Butterworth high-emphasis filter
Adds constant to frequency space.
Preserves image and sharpens edges.

30. Homomorphic Filtering (I)
Already known: Two images with different frequency composition that are added together can be separated with linear filtering.
Two images multiplied with each other?
Take logarithm first.
(subscript l indicates that Fourier transform has
been applied to Fourier transformed image)
Then filter, inverse Fourier transform
and reverse logarithm with exponent.

31. Homomorphic Filtering (II)
Extension for convolved images (Chapter 10.3).
generalized linear filtering.
Operations are called homomorphic systems.
With highpass filter used to achieve simultaneous dynamic range compression (brightness normalization) and contrast enhancement.

32. Homomorphic filtering
Original
Homomorphic filtering
Butterworth High-frequency emphasis filter, n = 1,
D = 0.6,
Ka = 0.1,
Kb = 0.5
Butterworth High-frequency emphasis filter, n = 1,
D = 0.6,
Ka = 0.1,
Kb = 0.5
Butterworth High-pass filter, n = 1,
D = 0.6

33. Adaptive-neighborhood Enhancement in General
Adaptive neighborhood (foreground):
Interconnected segment of pixels with certain common property with a seed pixel. (Found with seed fill.)
Properly defined segments should correspond to image features.
Found regions are extended to overlap with adjacent regions (background).
Borders of few pixels wide.
Prevents edge artifacts like reversed intensity across border.
Enhancing algorithm is performed within the combined foreground and background.
Result is applied to each seed pixel and each pixel within foreground with same value of property than seed.
Other pixels in foreground grow their own neighborhood.

34. Adaptive-neighborhood Contrast Enhancement
Common property: Similar gray value
To be exact: Growth tolerance .
If , all pixels connected to seed pixel with gray value between 0.95 and 1.05 times the seed pixel’s gray value are included to foreground.
All grown regions have contrast higher than independent of seed pixel’s gray value.
Worst case scenario
= average foreground pixel gray value
= average background pixel gray value
Weber’s ratio of 2 % (for contrast of visible features)
should be about 4 %.
Algorithm: Increase contrast to by replacing seed pixel’s value with
(From equation 2.7)
(From equation 2.7)

35. Adaptive- neighborhood contrast enhancement,
growth tolerance 0.05,
background depth 5
Original

36. Objective Assessment of Contrast Enhancement
Contrast histogram
Distribution of contrast of all possible regions obtained by seed fill algorithm.
Enhanced image should contain more counts of regions at higher contrast levels.
In practice same as more spread contrast histogram.
The second moment is used to characterize the spread

37. Image Enhancing - Ending Remarks
Better contrast
sharpness of detail and
visibility of features
are the targets for image enhancing.
Results can vary with each approach and image.
It can be beneficial to obtain several enhanced images with variety of approaches (as with most fields of image analysis).
Image restoration is presented in chapter 10.
Image restoration: reversing the degradation when the exact mathematical model of degradation is known.

38. Seed Fill - Foreground

39. Seed Fill - Background