Medical Image Compression with security & water marking
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
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
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
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
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
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
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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
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Theodoridis, "A Pattern Similarity Scheme for Medical Image Retrieval," IEEE Trans. Info. Tech in Biomedicine, vol 13, no.
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IEEE Trans. Image Processing, vol. 18, no. 12, Dec 2019.