Dissertation-1

1. Medical Image Compression with Security and Water Marking
by:
ANKIT KUMAR CHAUDHARY
(15/IEE/055)
Under The Guidance Of
Dr. M. A. Ansari
Department of Electrical Engineering
School of Engineering
Gautam Buddha University
Gautam Budh Nagar UP, India

2. Contents
1. Objective
2. Introduction and Overview
3. What are medical images ?
4. Why compress medical images?
5. Challenges unique to medical images
6. Techniques used
7. Future improvements
8. Security
9. Algorithm of Huffman Code
10. Flow Chart of Huffman Algorithm
11. Algorithm of DCT
12. Flow Chart of DCT Algorithm
13. Result
14. Conclusion
15. References

3. Objective
• To reduce the size of the medical image and add the security feature

4. Introduction and Overview
1. The field of image compression continues to grow at a rapid pace
2. As we look to the future, the need to store and transmit images will
only continue to increase faster than the available capability to
process all the data.
3. Image compression involves reducing the size of image data files,
while retaining necessary information

5. Block Diagram

6. Different Modalities
Modalities Typical Uncompressed Size
MRI 250MB
CT Scan 1GB
Nuclear Imaging 6MB
Ultrasound 38MB
Radiography 10MB
Digital Pathology 2.5GB

7. Applications that require image compression
1. Internet
2. Businesses
3. Multimedia
4. Satellite imaging
5. Medical imaging

8. What are medical images ?
Examples-
1. MRI (Magnetic resonance imaging)

9. Medical Images
2. Dynamic 3D Ultrasound
3. CT (computerized Tomograhpy)

10. Why compress medical images?
1. Growing need for storage
2. Efficient data transmission
3. Telemedicine
4. Tele-radiology applications
5. Real time Tele-consultation

11. Challenges unique to medical images
1. Compression Algorithms
2. Lossy / Lossless
3. Medical Images should always be stored in lossless format.
4. Erroneous Diagnostics and its legal implications.

12. Techniques used
Compression techniques may be classified into:
• Lossy
• Lossless
• Moreover, compression algorithms may be applied in the spatial
domain or frequency domain
Compressed image e.g. WinZIP
Transform to frequency
domain
Compressed image e.g. JPEG,
JPEG2000

13. There are two primary types of image
compression methods:
1. Lossless compression methods:
• Allows for the exact recreation of the original image data, and can
compress complex images to a maximum 1/2 to 1/3 the original
size – 2:1 to 3:1 compression ratios
• Preserves the data exactly

14. 2. Lossy compression methods:
• Data loss, original image cannot be re-created exactly
• Can compress complex images 10:1 to 50:1 and retain high
quality, and 100 to 200 times for lower quality, but acceptable
images.

15. • Low motion areas  lossy
• High motion areas  lossless
Future improvements
Lossless
Lossy

16. Security
1)RSA(Rivest-Shamir-Adelman)
• It is mainly used to encrypt a text or image lacking the requirement to
interchange a secret key individually.

17. 2)Watermarking
• Visible watermarking
Watermarked
image
Original image
minus watermark
Watermark

18. • Invisible watermark
Example: DCT‐based watermarking
Watermarked
images
Extracted
robust
invisible
watermark

19. Algorithm of Huffman Code
1) Create sorted nodes based on probability/frequency
2) Start loop
3) Find & remove two smallest probability node
4) Create new node[W[Node]=W[N1]+W[N2]]
5) Insert new node, back to sorted list.
6) Repeat the loop until only one last node is present in the list

20. Flow Chart of Huffman Algorithm

21. Algorithm of DCT
1) Read the image as a matrix.
2) Divide the matrix in block of 8x8.
3) Working from left to right, top to bottom, the DCT is applied to each
block.
4) Each block compressed through the quantization.
5) The array of compressed blocks that constitute the image is stored
in a drastically reduce amount of space.

22. Flow Chart of DCT Algorithm
Read the image as a matrix
Divide the matrix in blocks of 8x8,from left to
right & top to bottom
Quantization
Apply DCT
Obtain compress image
Start
Stop

23. Result
Fig.1-Servical spine

24. Fig.2: Ultrasound display by lossless technique

25. Fig. 3: Knee display output by lossless technique

26. Lossy Image Compression
Original image Compress image
Size-27.4KB Size-21.6KB
Fig. 4:Servical spine by lossy technique

27. Original image Compress image
Size-5.19KB Size-3.25KB
Fig. 5: Ultrasound output by lossy technique

28. Original image
Compress image
Size-34KB Size-4.17KB
Fig. 6: Knee display output by lossy technique

29. Conclusion
Parameters Lossless Technique Lossy Technique
Information Have information without losses Have information some
losses
Size Reduce size Reduce more size compare to
lossless
Transmission Harder to transmit compressed
file
Easy to transmit due to less
bandwidth

30. References
1. Yong Rui and Thomas S. Huang, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues," J Visual
Comm. And Image Representation, vol. 10, no. 4, Apr 2016.
2. Xin Yu Zhang and Tian Fu Wang, "Entropy- based Local Histogram Equalization for Medical Ultrasound Image
Enhancement," IEEE Intl. Con! 2015
3. Ivica Dimitrovski, Pero Guguljanov and Suzana Loskovska, "Implementation of Web Based Medical Image Retrieval
System in Oracle," IEEE 2nd Intl. Conference on Adaptive Science & Technology 2017.
4. H. Greenspan and A. T. Pinhas, "Medical Image categorization and retrieval for PACSusing the GMM-KL framework," IEEE
Trans. 1'110.Tech Biomedicine., vol. 11, no. 2, Mar. 2017.
5. Dimitris K. Iakovidis, Nikos Pelekis, Evangelos E. Kotsifakos, Ioannis Kopanakis, Haralampos Karanikas and Yannis
Theodoridis, "A Pattern Similarity Scheme for Medical Image Retrieval," IEEE Trans. Info. Tech in Biomedicine, vol 13, no.
4, Jul. 2018.
6. Hua Yuan and Xiao-Ping Zhang, "Statistical Modeling in the Wavelet Domain for Compact Feature Extraction and
Similarity Measure of Images," IEEE Trans. Circuits and Systems for Video Tech., vol. 20, no. 3, Mar 2018.
7. T. M. Lehmann, M. O. Guld, C. Thies, B. Plodowski, D. Keysers, B. Ott and H. Schubeert, "IRMA - Content based image
retrieval in medical applications," in Proc. 14th World Congr. Med. 1'110. (Medinfo), IDS, Amsterdam, The Netherlands,
vol. 2, 2019.
8. Sharadh Ramaswamy and Kenneth Rose, "Towards Optimal Indexing for Relevance Feedback in Large Image Databases,"
IEEE Trans. Image Processing, vol. 18, no. 12, Dec 2019.

31. Thank You