Total views : 339
A Hybrid Medical Image Compression Techniques for Lung Cancer
Objectives: This study focuses on Image compression and compares different methods. Methods/Statistical Analysis: In this work we simulated four image compression methods. The first method is focused on Karhunen-Loève Transforms (KLT), second method is focused on Walsh-Hadamard Transforms (WHT), third method based on FFT and fourth one is proposed sFFT. Findings: The experimental outcomes are compared with the different quality of parameters applying on numerous lung cancers CT scan images. The Proposed SFFT method algorithm was given better results like Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Mean Square Error (MSE) and Compression Ratio (CR) are compare to other Transform methods. Application /Improvement: The Proposed SFFT technique gives improved result compared with other methods in all evaluation measures.
CR, FFT, Image Compression, KLT, Lung Cancer CT Images, MSE, PSNR, Proposed sFFT, SC, WHT.
- Fukuoka M, Yano S, Giaccone G, Tamura T, Nakagawa K, Douillard JY, Nishiwaki Y, Vansteenkiste J, Kudoh S, Rischin D, Eek R. Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non–small-cell lung cancer. Journal of Clinical Oncology. 2003; 21(12):2237–46.
- Pretreatment evaluation of non–small-cell lung cancer. American Journal of Respiratory and Critical Care Medicine. 1997; 156(1):320–32.
- Mohammed AA, Hussein JA. Hybrid transform coding scheme for medical image application. IEEE ISSPIT 10’; 2010. p. 237–40.
- Thirumoorthi. C, Karthikeyan.T. A review on embedded zero wavelet transform coding. National Conference on Advanced Trends in Information Technology (NCATIT -2016); p. 40.
- Halpern MT, Gillespie BW, Warner KE. Patterns of absolute risk of lung cancer mortality in former smokers. Journal of the National Cancer Institute. 1993; 85(6):457–64.
- Roy AB, Dey D, Mohanty B, Banerjee D. Comparison of FFT, DCT, DWT, WHT compression techniques on electrocardiogram and photoplethysmography signals. International Conference on Computing, Communication and Sensor Network CCSN; 2012. p. 6–11.
- Abd-Elhafiez WM, Gharibi W. Color image compression algorithm based on DCT blocks. International Journal of Computer Science. 2012 Jul; 9(4):323–8.
- Singh H, Sharma S. Hybrid image compression using DWT, DCT and Huffman encoding techniques. International Journal of Emerging Technology and Advanced Engineering. 2012; 2(10):300–6.
- Gerbrands, Jan J. On the relationships between SVD, KLT and PCA. Pattern Recognition. 1981; 14(1):375–81.
- Patel NR, Kothari A. Performance analysis of medical image compression techniques. Proceedings of International Conference on ICT for Sustainable Development. Springer Singapore; 2016. p. 513–21.
- Mathur MK, Mathur G. Image compression using DFT through fast fourier transform technique. International Journal of Emerging Trends and Technology in Computer Science. 2012 Jul–Aug; 1(2).
- Al-Fayadh A, Hussain AJ, Lisboa P, Al-Jumeily D. An adaptive hybrid classified vector quantisation and its application to image compression. IEEE Computer Modeling and Simulation EMS'08. 2008; p. 253–6.
- Zhang SQ, Zhang SF, Wang XN, Wang Y. The image compression method based on adaptive segment and adaptive quantified. IEEE 3rd ICICIC'08; 2008. p. 353.
- Frigo M, Johnson SG. FFTW: An adaptive software architecture for the FFT. Proceedings of the ICASSP. 1998; 3:1381–4.
- Shrestha S, Wahid K. Hybrid DWT-DCT algorithm for biomedical image and video compression applications. IEEE 10th ICISSPA; 2010. p. 280–3.
- Sharma M. Compression using Huffman coding. International Journal of Computer Science and Network Security. 2010 May; 10(5):133–41.
- Bhooshan S, Sharma S. An efficient and selective image compression scheme using Huffman and adaptive Interpolation. IEEE 24th IVCNZ'09; 2009. p. 197–202.
- Xie Y, Jing X, Sun S, Hong L. A fast and low complicated image compression algorithm for predictor of JPEG-LS. IEEE IC-NIDC; 2009. p. 353–6.
- Srikanth S, Meher S. Compression efficiency for combining different embedded image compression techniques with Huffman encoding. ICCSP ’13; 2013. p. 816–20.
- Wei L. Research on image compression algorithm based on SPHIT. 3rd IEEE ICINIS; 2010. p. 104–7.
- Kadali KS, Rajaji L. FPGA and ASIC implementation of systolic arrays for the design of optimized median filter in digital image processing applications. Indian Journal of Science and Technology. 2014 Nov; 7(S7).
- Garge DM, Bapat VN. A low cost wavelet based mammogram image processing for early detection of breast cancer. Indian Journal of Science and Technology. 2009 Sep; 2(9).
- Baraiya N, Modi H. Comparative study of different methods for brain tumour extraction from MRI images using image processing. Indian Journal of Science and Technology. 2016 Jan; 9(4).
- Jadav RA, Patel SS. Application of singular value decomposition in image processing. Indian Journal of Science and Technology. 2010 Feb; 3(2).
- Karthikeyan T, Thirumoorthi C. Embedded Zero Tree Wavelet (EZW) algorithm based image transformation for easy optimization with HALIDE language. International Journal of Applied Engineering Research. 2015 Jun; 10(55):1551–4.
- Karthikeyan T, Thirumoorthi C. Easy optimization of image transformation using sFFT algorithm with HALIDE language. International Conference on “Contemporary Computing and Informatics (IC3I 2014); 2014; p. 1188–90.
- Karthikeyan T, Thirumoorthi C. A survey on embedded zero tree wavelet. International Journal of Computer Science. 2014 Oct; 2(2), No. 3:353–7.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.