1. Brightness Preserving Contrast
Enhancement of Medical Images
Debdoot Sheet
Computer Aided Medical Procedures, Technische Universität München, Germany
and
School of Medical Sc. and Tech., Indian Institute of Technology Kharagpur, India
(CAMPing 2012, 16 May 2012)

2. Outline
• General perception about Image Contrast
• Subjective contrast enhancement
• State of the Art
– Histogram Equalization (HE)
– Bi-histogram Equalization (BHE)
– Contrast Limited Adaptive Histogram Equalization
(CLAHE)
– Dynamic Histogram Equalization (DHE)
• Brightness Preserving Dynamic Fuzzy Histogram
Equalization (BPDFHE)
• Applications (Medical and others)
• Conclusion
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 2

3. Image Contrast; general perception
• Difference in visual properties of an object or its
representation in an image make it distinguishable from
other objects and the background
– Brightness, Color, Texture etc.
• This difference in the visual properties of objects and their
background are generally referred to as Contrast
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 3

4. Subjective Contrast Enhancement
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 4

5. State of the Art
Contrast Limited Adaptive
Histogram Equalization Histogram Equalization
• Simple to implement and fast • Operates on small image tiles
• Generally gives good • Each tile's contrast is enhanced,
performance over variety of so that the histogram of the
images. output region approximately
matches the histogram specified
• Introduces major changes in by a distribution. (Gaussian,
the image gray level when the Rayleigh, Poisson, etc.)
spread of the histogram is not • The neighboring tiles are
significant combined using bilinear
• Cannot preserve the overall interpolation to eliminate
image-brightness which is artificially induced boundaries.
critical to medical, surveillance • The contrast, especially in
and consumer electronics homogeneous areas, can be
limited to avoid amplifying any
applications. noise.
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 5

6. State of the Art
Bi-histogram Equalization Multi-histogram Equalization
• Partition histograms in two • Partition histogram in multiple
sub-histograms and equalize sub-histograms and equalize
them independently. them independently.
• Proposed to minimize mean • These techniques have been
intensity change. proposed to further improve
• Image parameters such as the mean image brightness
median, mean gray level etc. preserving capability.
selected grayscale threshold • Histogram features as local
used for partitioning.
peak or valley points act as
markers for partitioning of the
histogram.
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 6

7. State of the Art
Bi-histogram Equalization Multi-histogram Equalization
• Y. T. Kim, “Contrast Enhancement Using • D. Menotti, L. Najman, J. Facon, and A.A.
Brightness Preserving Bi-Histogram Araújo, “Multi-Histogram Equalization
Equalization”, IEEE TCE, vol. 43, no. 1, pp. 1- Methods for Contrast Enhancement and
8, 1997. Brightness Preserving”, IEEE TCE, Vol. 53,
• S. D. Chen and A. R. Ramli, “Minimum Mean No. 3, Aug 2007.
Brightness Error Bi-Histogram Equalization in • M. Abdullah-Al-Wadud, et al, “A Dynamic
Contrast Enhancement”, IEEE TCE, vol. 49, Histogram Equalization for Image Contrast
no. 4, pp. 1310-1319, Nov. 2003. Enhancement”, IEEE TCE, vol.53, no. 2, pp.
• Yu Wan, Qian Chen and Bao-Min Zhang., 593–600, May 2007.
“Image Enhancement Based On Equal Area • H. Ibrahim, and N. S. P. Kong, “Brightness
Dualistic Sub-Image Histogram Equalization Preserving Dynamic Histogram Equalization
Method,” IEEE TCE, vol. 45, no. 1, pp. 68-75, for Image Contrast Enhancement”, IEEE TCE,
Feb. 1999. vol. 53, no. 4, pp. 1752–1758, Nov. 2007.
• S.-D. Chen and A. Ramli, “Contrast • C. Wang and Z. Ye, “Brightness Preserving
enhancement using recursive MeanSeparate Histogram Equalization with Maximum
histogram equalization for scalable brightness Entropy: A Variational Perspective”, IEEE
preservation,” IEEE TCE, vol. 49, no. 4, pp. TCE, vol. 51, no. 4, pp. 1326-1334, Nov. 2005.
1301-1309, Nov. 2003.
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 7

8. Brightness Preserving Dynamic Fuzzy
Histogram Equalization
Low Contrast Image h( v ) ← h( v ) + ∑∑ξF ( i , j ) ,v
~
i j
Fuzzy Histogram  F ( i, j ) − v 
ξ F ( i , j ) ,v = max 0,1 −
~



Computation  α 
BPDFHE Stages
Partitioning of the
Histogram
Dynamic Equalization of the
Histogram Partitions
Normalization of Image
D. Sheet, H. Garud, A. Suveer, M. Mahadevappa,
Brightness and J. Chatterjee, “Brightness preserving dynamic
fuzzy histogram equalization,” IEEE TCE, vol. 56,
Contrast Enhanced Image
no. 4, pp. 2475 –2480, Nov. 2010.
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 8

9. BPDFHE
Rangek =
( L − 1) × ( highk − lowk ) × log10 Pk
∑i=1 ( highi − lowi ) × log10 Pi
n +1
v
h( i )
v′ = Start k + Rangek × ∑
i = Start k Pk
k
Stopk = ∑ Rangei
i =1
k −1
Start k = ∑ Rangei + 1
i =1
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 9

10. Performance
Original HE
CLAHE BPDFHE
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 10

11. Comparison Metrics
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 11

12. Handling Color Images
Low Contrast Color
H. Garud, D. Sheet, P. K. Karri, A. Suveer,
Image
J. Chatterjee, M. Mahadevappa, A. K. Ray,
“Brightness Preserving Contrast
Color Space Conversion Enhancement in Digital Pathology” Proc.
(RGB to CIE L*a*b*) ICIIP -2011, Shimla, India, Dec. 2011.
BPDFHE in L* Channel
Color Space Conversion
(CIE L*a*b* to RGB)
Contrast Enhanced Color
Image
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 12

13. Performance
Original HE
CLAHE BPDFHE
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 13

14. Medical application
Digital Microscopy Equipment with Image Acquisition, Image Analysis and
Network Communication, US Patent Application 12/979,398, filed on 28 Dec. 2010.
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 14

15. Other applications
Digital and Print Media
Digital and Print Media Surveillance
Surveillance
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 15

16. Conclusion
• Contrast enhancement capability of BPDFHE limits
when trying to preserve brightness.
• Performance is still comparable and often better than
that of the HE technique.
• By virtue of operating on the global statistics of images
BPDFHE is computationally more efficient than CLAHE.
• CLAHE, though able to increase the contrast more than
other techniques compared, it introduces large changes
in the pixel gray levels. This may lead to introduction of
the processing artifacts and affect the decision making
process.
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 16

17. Thank You!
D. Sheet H. Garud A. Suveer M. Mahadevappa J. Chatterjee A. K. Ray
Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 17