contrast enhancement of mammogram images

1. Design and FPGA Implementation of Contrast
Enhancement on Mammogram Images for Early
Detection of Breast Cancer
Ranjitha.S

2. AGENDA
 Introduction
 Motivation and objectives
 Proposed method
a. Block diagram
b. Simulation flow
 Experimental results
 Present work
 Tools used
 References

3. Introduction
 Mammography
 Contrast enhancement

4. Courtesy

5. Motivation
 In India, the number of new breast cancer cases is about
115,000 per year and this is expected to rise to 250,000
new cases per year by 2015.
 Each year 10.9 million people suffer from breast cancer
worldwide that results in 6.7 million deaths from the
disease.

8. Objective
 This project deals with the implementation of an algorithm for
breast cancer detection using FPGA.
 This project aims at contrast enhancement of mammographic
images for early detection of breast cancer, optimal contrast
without losing any local information of the mammogram
image.
 To enhance the medical image like Computed Tomography
(CT), Magnetic Resonance Imaging (MRI),X-ray medical
image so as
~ to improve its visual quality,
~ and to help the doctors in more accurate diagnosis
of patients with ease

9. Literature review
Optimal Contrast enhancement for detection of masses and micro calcification of
mammogram images using CLAHE based on local contrast modification (LCM).
Proposed to highlight the finer hidden details in mammogram images and to adjust
the level of contrast enhancement.
Advantages :
 Better contrast enhancement and information preservation.
 All types of mammogram images like fatty, fatty glandular and dense glandular can
be enhanced effectively.
__________________________________________________________________________
_
Shelda Mohan and M. Ravishankar, Dayananda Sagar College of Engineering, Bangalore,
India, Modified Contrast Limited Adaptive Histogram Equalization Based on Local
Contrast Enhancement for Mammogram Images. Springer-Verlag Berlin Heidelberg 2013

10. Continued..
 The application of a global transform or a fixed operator to an entire image
often yields poor results in at least some parts of the given image.
 Morrow has proposed a region based technique for improvement of results.
 Keeping in view, the shortcomings of the pre-build techniques, a modified
algorithm is proposed based upon the adaptive region growing technique.
proposed algorithm, Adaptive approach & Linear Stretching
Advantages:
 Image more precisely in comparison to Adaptive HE & Linear
Stretching.

11. Proposed Methodology
 Histogram equalization
 Contrast enhancement
 FPGA implementation

12. Block Diagram
Read input
image
Histogram
equalization in
matlab
Comparison of
histogram
equalized
images
FPGA
implementation
Compare overall
output images of
FPGA &
MATLAB

13. Simulation Flow
Resize the input
image to max level
Perform
histogram
equalization of
resized image by
global operation
Down sample the
image
Perform
histogram
equalization to
lowest block of
divided image
Reconstruct
histogram equalized
blocks of images to
preferred size
Display the global
and reconstructed
local histogram
equalized image
with PSNR
Input
mammogram
image
Compare the
output images of
global and local
operation

14. Experimental Results

15. A. Original image B. Global histogram
equalized image
C. Local histogram
equalized image
10.5551 10.5267PSNR

16. 15.3576
15.3944
A. Original image B. Global histogram
equalized image C. Local hist equalized
image
PSNR

17. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 16.6412 16.6412

18. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 14.6094 14.6121

19. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 13.6675 13.6771

20. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR
7.4666 7.4078

21. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 6.6036 6.6338

22. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 13.4422 13.4378

23. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 9.5350 9.5032

24. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 13.3386 13.3384

25. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 13.5664 13.5581

26. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 12.7675 12.8350

27. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 8.7724 8.7494

28. A. Original image B. Global histogram
equalized image
C. Local hist equalized
image
PSNR 6.0407 6.0575

29. Tools Used
 Matlab 2008
 Modelsim 2008

30. Advantages
 Improved image quality
 Better than the original image
 Easy to develop algorithm

31. References
[1] S. Jayaraman, S. Esakkirajan And T.Veerakumar ,Digital
Image Processing, August 10, 2013.
[2] Hajar Moradmand, Saeed Setayeshi, Alireza Karimian, Mehri
Sirous. Iranian Journal of Medical Physics. Contrast
Enhancement of Mammograms for Rapid Detection of
Microcalcification Clusters, October 1, 2013.
[3] Shelda Mohan and M. Ravishankar, Dayananda Sagar College
of Engineering, Bangalore, India, Modified Contrast Limited
Adaptive Histogram Equalization Based on Local Contrast
Enhancement for Mammogram Images. Springer-Verlag Berlin
Heidelberg 2013.

32. Continued
 [6] Asadollah Shahbahrami, Jae Young Hur, Ben Juurlink, and
Stephan Wong, Netherlands, Iran, FPGA Implementation of
Parallel Histogram Computation.
 [7] Nitin Sachdeva, Tarun Sachdeva,YMCA University of
Science & Technology, India. An FPGA Based Real-time
Histogram Equalization Circuit for Image Enhancement, IJECT
Vol. 1, Issue 1, December 2010.
 [8]Junguk Cho, Seunghun Jin, Key Ho Kwon and Jae Wook
Jeon, Sungkyunkwan University, Korea, A Real-Time
Histogram Equalization System with Automatic Gain Control
Using FPGA, 4 August 2010.

33. Continued
 [9] Dr. S. Ramachandran , Indian Institute of Technology
Madras, India, Digital VLSI Systems Design A Design Manual
for Implementation of Projects on FPGAs and ASICs Using
Verilog. 1994-2007.pg no.417-479.
 [10]Donald G. Bailey Massey University, New Zealand,
“DESIGN FOR EMBEDDED IMAGE PROCESSING ON
FPGAS”.pg no.199-273.
 [11] William Mark Morrow, Raman Bhalachandra Paranjape,
Rangaraj M. Rangayyan and Joseph Edward Leo Desautels.
Region-Based Contrast Enhancement of Mammograms.
September 1992.