Histogram-based contrast enhancement for mammogram images.

1. HISTOGRAM BASED CONTRAST
ENHANCEMENT FOR
MAMMOGRAM IMAGES

2. 1
Why another contrast enhancement technique?
• Standard HE results in excessive contrast enhancement.
• Lack of control of enhancement level.
• Distortion in local details.
Proposed Technique for contrast enhancement
Histogram
Modification
Contrast Limited
Adaptive Histogram
Equalization
Input
Image
Output
Image
Database and Evaluation method used
• Mammographic Image Analysis Society (MIAS) database.
• Each grayscale image is 1024 x 1024 pixels.
• Enhancement Measure (EME) as evaluation method.

3. 2
Histogram
0 7 3 2 3
0 0 0 6 7
7 7 2 2 0
1 1 0 4 1
0 0 7 4 1
Image matrixImage
0 1 2 3 4 5 6 7
Number of pixels of
intensity r
Histogram plots the number of pixels for each intensity value.
h(r)
Intensity values

4. 3 Histogram Modification
• Increase the potentiality of image contrast enhancement.
• Resultant image to be more relevant to input image.
• Multi-objective optimization problem
Solution of the above problem
- modified histogram
- original (input) histogram
- uniform histogram
- enhancement parameter
0 < < ∞

5. 4
Histogram Modification
Fig. mapping for enhanced images with
ѱ = [0 1] – 10 values.
Fig. mapping for enhanced images with
ѱ = 0, 0.1, 0.5 and 1from top respectively
Mapping function used
𝑇 𝑛 = ( 2 𝐿
− 1 ) ෍
𝑗=0
𝑛
𝑝 𝑗 𝑤ℎ𝑒𝑟𝑒 𝑝 𝑗 𝑖𝑠 𝑝𝑑𝑓 𝑜𝑓 𝑚𝑜𝑑𝑖𝑓𝑖𝑒𝑑 ℎ𝑖𝑠𝑡𝑜𝑔𝑟𝑎𝑚

6. 5
Contrast Limited Adaptive Histogram Equalization(CLAHE)
• Unlike HE, CLAHE operates on small regions called tiles in the
images.
• Contrast adjust according to their neighborhood pixels.
• Distribution parameter is used to define the shape of the
histogram.
• Original histogram is clipped and clipped pixels are redistributed
to each gray level.
• Selecting appropriate clipping level, undesired noise amplification
can be reduced.
• Different from AHE in terms of its contrasting limit.
• Neighboring tiles are combined using bilinear interpolation to
eliminate artificially induced boundaries.
• Clipping factor used in this experiment is 0.02

7. Obtain all
input values
Divide image into
contextual
regions/tiles
Enhancement
process applied
over tiles
Generate gray
level mapping
Apply mapping to
the image
Input
Image
Output
Image
CLAHE Algorithm
6

8. 7
Results and Performance Measure
• The proposed methodology generates strong contrast enhancement.
• All the local information of original image are preserved.
• For this experiment ѱ = 0.8 and clipping factor = 0.02
• Performance is measured using Enhancement measure(EME).
• Ideally should not be too high or too low.
Fig. EME for MIAS database

9. 8
Experimental Results
Fig. Left to Right: Original image, HE,USM, CLAHE, HM-CLAHE

10. 7
THANKS