Hybrid medical image compression using the IKL transform with an efficient encoder

Document Type : Research Paper

Authors

Department of Computer Science and Engineering, Karunya Institute of Technology and sciences, India.

Abstract

Medical images are generated in a huge number in the research centers and hospitals every day. Working with the medical images and maintain the storage needs an efficient fund and huge storage space. Retaining the quality of the medical image is also very essential. Image compression without losing its quality is the only term to achieve the desired task. Achieving the desired task using the integer Karhunen Loeve transform attains a quality output and also with less storage space. JPEG and JPEG 2000 are also challenging to the integer transform based compression. Resulting the compression quality in terms of peak signal noise ratio, compression ratio is attained. Proposed method of compression is compared with the other efficient algorithms. Thus this proposed method can be used efficiently for the medical image in order to store and retrieve in healthcare industry.

Keywords

[1] A. A. Mohammed and J. A. Hussein, Hybrid transform coding scheme for medical image application, IEEE Int.
Symp. Signal Process. Inf. Technol. ISSPIT (2010) 237–240.
[2] A. J. Hussain, A. Al-Fayadh and N. Radi, Image compression techniques: A survey in lossless and lossy algorithms,
Neurocomput. 300 (2018) 44–69.
[3] A. Mofreh, A new lossless medical image compression technique using hybrid prediction model, Signal Process.
An Int. J. 10(3) (2016) 20–30.
[4] D. Zhang and S. Chen, Fast image compression using matrix K-L transform, Neurocomput. 68(1-4) (2005) 258–
266.
[5] I. Blanes and J. Serra-sagrist, Clustered reversible-KLT for progressive lossy-to-lossless 3d image coding *, Data
Compression Conf. (2009) 233–242.
[6] K. R. Rao and P. C. Yip, The transform and data compression handbook, Transform Data Compression Handbook,
2000.
[7] L. Galli and S. Salzo, Lossless hyperspectral compression using KLT, Int. Geosci. Remote Sens. Symp. 1 (2004)
313–316.
[8] N. B. Harikrishnan, V. V. Menon, M. S. Nair and G. Narayanan, Comparative evaluation of image compression
techniques, Int. Conf. Algorithms, Methodol. Model. Appl. Emerg. Technol. ICAMMAET 2017, vol. -Janua,
(2017) 1–4.
[9] P. Hao and Q. Shi, Matrix factorizations for reversible integer mapping, IEEE Trans. Signal Process. 49(10)
(2001) 2314–2324.
[10] P. L. Dragotti, G. Poggi and A. R. P. Ragozini, Compression of multispectral images by three-dimensional SPIHT
algorithm, IEEE Trans. Geosci. Remote Sens. 38(1) (2000) 416–428.
[11] P. N. Tackie Ammah and E. Owusu, Robust medical image compression based on wavelet transform and vector
quantization, Inf. Med. Unlocked, 15 (2019) 100183.
[12] P. Waldemar and T. A. Ramstad, Hybrid KLT-SVD image compression, ICASSP, IEEE Int. Conf. Acoust. Speech
Signal Process. - Proc., 4 (1997) 2713–2716.
[13] R. Smith-Bindman, D. L. Miglioretti and E. B. Larson, Rising use of diagnostic medical imaging in a large
integrated health system, Health Aff. 27(6) (2008) 1491–1502,
[14] R. M. Thanki and A. Kothari, Hybrid and advanced compression techniques for medical images, Springer International Publishing, 2019.
[15] R. Nagendran and A. Vasuki, Hyperspectral image compression using hybrid transform with Different waveletbased transform coding, Int. J. Wavelets, Multiresolution Inf. Process. 17(2) (2019) 1–21.
[16] S. A. Wilson and A. A. Farag, Image compression using neural networks, Intell. Eng. Syst. Through Artif. Neural
Networks, 5 (1995) 503–508.
[17] S. Juliet, E. Blessing and K. Ezra, A novel medical image compression using Ripplet transform, J. Real-Time
Image Process. (2016) 401–412.
[18] S. S. Bhairannawar and S. Sarkar, Implementation of optimized Karhunen – Loeve transform for image processing
applications, J. Real-Time Image Process. 17(2) (2020) 357–370.
[19] T. Karthikeyan and C. Thirumoorthi, A hybrid medical image compression techniques for lung cancer, Indian J.
Sci. Tech. 9(39) (2016).
[20] L. Wang , J. Wu , L. Jiao and G. Shi, 3D Medical image compression based on multiplierless low- complexity
rklt and shape-adaptive wavelet transform, Key Laboratory of Intelligent Perception and Image Understanding of
Ministry of Education, (2009) 2521-2524.
Volume 12, Issue 2
November 2021
Pages 757-767
  • Receive Date: 22 February 2021
  • Revise Date: 15 March 2021
  • Accept Date: 12 April 2021