Modified Firefly Model-Based Vector Quantization for Clinical Medical Image Compression

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 9

Year of Publication : 2023

Authors : Preethi, Clara Shanthi, G. Kadiravan, Rajkumar N, Viji C, Prabhu Shankar B

10.14445/23488549/IJECE-V10I9P101

How to Cite?

Preethi, Clara Shanthi, G. Kadiravan, Rajkumar N, Viji C, Prabhu Shankar B, "Modified Firefly Model-Based Vector Quantization for Clinical Medical Image Compression," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 9, pp. 1-9, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I9P101

Abstract:

Due to the rapid increase in the usage of medical images for disease diagnosis and the rise in the volume of data produced by different medical imaging equipment, the transmission and archival of images need data compression. In the past decade, various image compression methods have been presented and find its applicability in various fields. Vector Quantization (VQ) plays a vital part in compressing images, and a Quantization Table (QT) construction is a significant process. The effectiveness of any compression technique mainly relies on the QT, generally a matrix of 64 integers. Selecting a QT is an optimization issue that bio-inspired techniques can address. The article compares two QT selection algorithms: Firefly with the Tumbling effect (FF-Tumbling) and the Teaching and Learning Based Optimization (FF-TLBO) approach. An extensive study is made between these two methods and analyzed the results. The simulation outcome is interesting in that the FF-Tumbling approach can achieve optimal reconstructed image quality, and the FF-TLBO method has the efficiency to achieve optimal compression performance.

Keywords:

Medical imaging, Quantization, Firefly, Compression, FF-TLBO.

References:

[1] R. Duszak, and L. Harvey, “Medical Imaging: Is the Growth Boom over? The Neiman Report,” Neiman Health Policy Institute, 2012.
[Google Scholar] [Publisher Link]
[2] Martey S. Dodoo, Richard Duszak, and Danny R. Hughes, “Trends in the Utilization of Medical Imaging from 2003 to 2011: Clinical Encounters Offer a Complementary Patient-Centered Focus,” Journal of the American College of Radiology, vol. 10, no. 7, pp. 507-512, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vaishali Patel, and Anand Mankodia, “Deep Learning in Medical Image Super-Resolution: A Survey,” International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 1-12, 2023.
[CrossRef] [Publisher Link]
[4] P. Kanmani, P. Marikkannu, and M. Brindha, “A Medical Image Classification Using ID3 Classifier,” SSRG International Journal of Computer Science and Engineering, vol. 3, no. 10, pp. 8-11, 2016.
[CrossRef] [Publisher Link]
[5] M. Amirthalingam, and R. Ponnusamy, “Automated Wireless Capsule Endoscopy Image Classification Using Reptile Search Optimization with Deep Learning Model,” International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 274-283, 2023.
[CrossRef] [Publisher Link]
[6] Janani S., and D.F.X. Christopher, “Conditional Super Resolution Generative Adversarial Network for Cervical Cell Image Enhancement,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10, no. 4, pp. 70-76, 2023.
[CrossRef] [Publisher Link]
[7] V. Baby Deepa, and R. Malathi, “A Review of Image Processing in Different Techniques,” International Journal of Computer and Organization Trends, vol. 10, no. 5, pp. 12-15, 2020.
[CrossRef] [Publisher Link]
[8] I.W. Van Aken et al., “Compressed Medical Images and Enhanced Fault Detection within an ARC-NEMA Compatidle Picture Archiving and Communications System,” Medical Imaging, vol. 767, 1987.
[CrossRef] [Google Scholar] [Publisher Link]
[9] David A. Clunie, “What is Different about Medical Image Compression,” IEEE Communication Society-Multimedia Communications Technical Committee E-Letter, vol. 6, no. 7, pp. 31-37, 2011.
[Google Scholar]
[10] Chin-Chen Chang, Yu-Chiang Li, and Jun-Bin Yeh, “Fast Codebook Search Algorithms Based on Tree-Structured Vector Quantization,” Pattern Recognition Letters, vol. 27, no. 10, pp. 1077-1086, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yogesh Kumar Sharma, Amit Mishra, and Amit Sharma, “An Advanced Approach for Image Steganography Method,” International Journal of Computer and Organization Trends, vol. 9, no. 4, pp. 1-4, 2019.
[CrossRef] [Publisher Link]
[12] Giuseppe Patané, and Marco Russo, “The Enhanced LBG Algorithm,” Neural Networks, vol. 14, no. 9, pp. 1219-1237, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Nasir Rajpoot et al., “A Novel Image Coding Algorithm Using Ant Colony System Vector Quantization,” International Workshop on Systems, Signal and Image Processing, Poland, 2004.
[Google Scholar] [Publisher Link]
[14] Kaggle, Diabetic Retinopathy Detection, Dataset Description, 2015. [Online]. Available: https://www.kaggle.com/c/diabetic-retinopathy-detection/dat