Transcript Image Enhancement Biomedical Image Analysis Rangaraj M. Rangayyan course: biomedical image processing vibhor kumar Hannu Laaksonen Topics to be covered 1) Convolution mask Operations .

Image Enhancement
Biomedical Image Analysis
Rangaraj M. Rangayyan
course:
biomedical image
processing
vibhor kumar
Hannu Laaksonen
Topics to be covered
1) Convolution mask Operations
. unsharp masking
. Sobtracting Laplacian
2) High Frequency Emphasis
3) Homomorphic filtering for Enhancement
4) Adaptive Contrast enhancement
Convolution Mask Operators - Unsharp masking
The Generalized equation of unsharp masking is
f(m,n) = [g(m,n) - µg(m,n)] +  g(m,n)
blurred image
Is calculated as average of the pixels in
the window taken around the pixel(m,n)
The weight  can be changed according to desired effect.
For e.g. For a 3X3 convolution mask the unsharp masking is given by
- 1
- 1
- 1
8
- 1
8
- 1
8
8
8
- 1
8
- 1
8
2
- 1
8
Subtracting Laplacian
The degraded image may be expressed in a Taylor series as
2 2 g
g

g(x,y,) = g(x,y,0) + 
(x,y,t) + …...
2 t2
t
Taking g = k2g using
t
diffusion model
2
We get fe = g - k g
For k = 1 and mask 3X3 we get subtracting Laplacian as:
0 -1 0
Unlike Laplacian subtracting Laplacian
maintain the intensity information while
-1 5 -1
making the image sharp
0 -1 0
(a)
(b)
(a) original lena image
(b) Laplacian
(c) Unsharp masking
(d) subtracting laplacian
(c)
(d)
High-frequency Emphasis
Highpass filtering are useful in detecting edges but for enhancing the images it
is necessary to maintain the intensity information.
High-emphasis filter does the image enhance keeping the intensity information.
The Butterworth high-emphasis filter can termed as:
H(u,v) = 1 +
2
1 + (sqrt(2)-1 )
Filter gain
Frequency
2n
D0
D(u,v)
(a) Original shape image
(b) the ideal high pass
filter
(c) The Butter worth
highpass filter
(d) the Butterworth highemphasis filter
Enhancement using Homomorphic filtering
transform
input
image
linear filtering and
enhancement
inverse
transform
filtered
image
(a) Original Image
(b) log transform of original
image
(c) Homomorphic filtering
including a Butterworth
high-emphasis filter
(d) Butterworth high
imphasis filter only
Graphical Models and Image
Processing, 54(3):259267,May 1992
Adaptive Contrast Enhancement
Adaptive-neighborhood contrast enhancement:
(1) non overlapping regions segmentation
(2) Overlapping regions segmentation
seed fill region growing:
The region consists of spatially connected pixels that falls that fall within the
specified gray level devaiation from seed pixel.
every time data is devided into back ground and foreground pixels
The growth tolerance threshold  is highly important factor
Adaptive Contrast Enhancement
contrast enhancment can be done using the formula
fe = b (1+Ce)/ (1-Ce)
increased contrast
mean background value
(a) Part of mammogram with a
cluster of calcification
(b) adaptive-neighborhood contrast
enhancement
(c) gamma correction
(d) unsharp masking
IEEE Transcation on Medical imaging
11(3):392-406,1992
Topics covered
1) Convolution mask Operations
. unsharp masking
. Sobtracting Laplacian
2) High Frequency Emphasis
3) Homomorphic filtering for Enhancement
4) Adaptive Contrast enhancement