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)

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

Image Contrast; general perception

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 3

• 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

Subjective Contrast Enhancement

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

State of the Art

Histogram Equalization • Simple to implement and fast

• Generally gives good performance over variety of images.

• Introduces major changes in the image gray level when the spread of the histogram is not significant

• Cannot preserve the overall image-brightness which is critical to medical, surveillance and consumer electronics applications.

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 5

Contrast Limited Adaptive Histogram Equalization • Operates on small image tiles • Each tile's contrast is enhanced,

so that the histogram of the output region approximately matches the histogram specified by a distribution. (Gaussian, Rayleigh, Poisson, etc.)

• The neighboring tiles are combined using bilinear interpolation to eliminate artificially induced boundaries.

• The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise.

State of the Art

Multi-histogram Equalization

• Partition histogram in multiple

sub-histograms and equalize

them independently.

• These techniques have been proposed to further improve the mean image brightness preserving capability.

• Histogram features as local

peak or valley points act as

markers for partitioning of the

histogram.

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 6

Bi-histogram Equalization

• Partition histograms in two sub-histograms and equalize them independently.

• Proposed to minimize mean intensity change.

• Image parameters such as median, mean gray level etc. selected grayscale threshold used for partitioning.

State of the Art

Bi-histogram Equalization • Y. T. Kim, “Contrast Enhancement Using

Brightness Preserving Bi-Histogram Equalization”, IEEE TCE, vol. 43, no. 1, pp. 1-8, 1997.

• S. D. Chen and A. R. Ramli, “Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement”, IEEE TCE, vol. 49, no. 4, pp. 1310-1319, Nov. 2003.

• Yu Wan, Qian Chen and Bao-Min Zhang., “Image Enhancement Based On Equal Area Dualistic Sub-Image Histogram Equalization Method,” IEEE TCE, vol. 45, no. 1, pp. 68-75, Feb. 1999.

• S.-D. Chen and A. Ramli, “Contrast enhancement using recursive MeanSeparate histogram equalization for scalable brightness preservation,” IEEE TCE, vol. 49, no. 4, pp. 1301-1309, Nov. 2003.

Multi-histogram Equalization • D. Menotti, L. Najman, J. Facon, and A.A.

Araújo, “Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving”, IEEE TCE, Vol. 53, No. 3, Aug 2007.

• M. Abdullah-Al-Wadud, et al, “A Dynamic Histogram Equalization for Image Contrast Enhancement”, IEEE TCE, vol.53, no. 2, pp. 593–600, May 2007.

• H. Ibrahim, and N. S. P. Kong, “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement”, IEEE TCE, vol. 53, no. 4, pp. 1752–1758, Nov. 2007.

• C. Wang and Z. Ye, “Brightness Preserving Histogram Equalization with Maximum Entropy: A Variational Perspective”, IEEE TCE, vol. 51, no. 4, pp. 1326-1334, Nov. 2005.

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 7

Brightness Preserving Dynamic Fuzzy Histogram Equalization

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 8

Fuzzy Histogram Computation

Partitioning of the Histogram

Dynamic Equalization of the Histogram Partitions

Normalization of Image Brightness

Low Contrast Image

Contrast Enhanced Image

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D. Sheet, H. Garud, A. Suveer, M. Mahadevappa, and J. Chatterjee, “Brightness preserving dynamic fuzzy histogram equalization,” IEEE TCE, vol. 56, no. 4, pp. 2475 –2480, Nov. 2010.

BPDFHE

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Performance

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 10

Original

CLAHE

HE

BPDFHE

Comparison Metrics

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

Handling Color Images

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Color Space Conversion (RGB to CIE L*a*b*)

BPDFHE in L* Channel

Color Space Conversion (CIE L*a*b* to RGB)

Contrast Enhanced Color Image

Low Contrast Color Image

H. Garud, D. Sheet, P. K. Karri, A. Suveer, J. Chatterjee, M. Mahadevappa, A. K. Ray, “Brightness Preserving Contrast Enhancement in Digital Pathology” Proc. ICIIP -2011, Shimla, India, Dec. 2011.

Performance

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 13

Original

CLAHE

HE

BPDFHE

Medical application

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 14

Digital Microscopy Equipment with Image Acquisition, Image Analysis and Network Communication, US Patent Application 12/979,398, filed on 28 Dec. 2010.

Other applications

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 15

Digital and Print Media Surveillance

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

Thank You!

Brightness Preserving Contrast Enhancement of Medical Images - Debdoot Sheet (CAMPing 2012) 17

D. Sheet H. Garud A. Suveer M. Mahadevappa J. Chatterjee A. K. Ray