NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION

Authors

  • G Vimala Kumari jntuk
  • G Sasibhushana Rao Andhra University College of Engineering, Visakhapatnam-530003, India
  • B Prabhakara Rao Jawaharlal Nehru Technological University, Kakinada, Kakinada-533003, Andhra Pradesh, India

DOI:

https://doi.org/10.5566/ias.1865

Keywords:

Bacteria Foraging Optimization Algorithm (BFOA), Grey Wolf Optimization (GWO), Kapur’s entropy, Moth-flame Optimization (MFO) Algorithm, Particle Swarm Optimization (PSO), Renyi’s entropy

Abstract

For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.

Author Biographies

G Vimala Kumari, jntuk

G. Vimala Kumari received B.Tech. in 2006 from JNTU Hyderabad and M.Tech. in 2010 from JNTU Kaikinada, Andhra Pradesh, India. Currently, she is pursuing Ph.D. in JNTU Kaikinada, India and is working as Assistant Professor in the department of Electronics and Communication Engineering, MVGR College of Engineering, Vizianagaram, India. Her research interests include Image processing, Communication Engineering and Embedded systems.

G Sasibhushana Rao, Andhra University College of Engineering, Visakhapatnam-530003, India

Prof. Gottapu Sasibhushana Rao is Professor in the Department of Electronics & Communication Engineering, Andhra University College of Engineering, Visakhapatnam, India. He is a senior member of IEEE, fellow of IETE, member of IEEE communication Society, Indian Geophysical Union (IGU) and International Global Navigation Satellite System (IGNSS), Australia. Prof. Rao was also the Indian member in the International Civil Aviation organization (ICAO), Canada working group for developing SARPS. He has published more than 485 Technical and research papers in different National / International conferences and Journals. His current research areas includes cellular and mobile communication, GPS, Bio medical and signal processing, under water image processing and optimization techniques.

B Prabhakara Rao, Jawaharlal Nehru Technological University, Kakinada, Kakinada-533003, Andhra Pradesh, India

Dr. B. Prabhakara Rao, received Ph.D. from Indian Institute of Science, Bangalore, did his B.Tech. and M.Tech from S.V.University. He is presently working as Programme Director, School of nanotechnology, JNTU, Kakinada, Andhra Pradesh, India. He has more than 35 years of teaching experience and more than 240 publications to his credit. He held different positions in his career as Head of the Department and Vice Principal, Director in Institute of Science and Technology, Director of Evaluation, Director of Foreign Universities Relations, Director-Admissions during in the years 2001 to 2013, and as Rector from July 2013 to 2017 in the JNT University, Kakinada. His research interests include optical communications, wireless communications, microwave  communications and image processing.

References

Anitha V, Pallikonda R, Vishnuvarthanan G, Yudong Z, Arunprasath T (2017). An automated hybrid approach using clustering and nature inspired optimization technique for improved tumour and tissue segmentation in magnetic resonance brain images, Applied Soft Computing, 57: 399–26.

Badri NS, Veerakumar T, Esakkirajan SA (2016). Tumour or abnormality identification from magnetic resonance images using statistical region fusion based segmentation, Magnetic Resonance Imaging, 34(9): 1292-1304.

Chen-Kuei Y, Wen-Hsiang. T (2015). Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle, Pattern Recognition Letters, 19: 205-15.

Diego O, Salvador H, Erik C, Gonzalo P, Omar A, Jorge G (2017). Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm, Expert Systems with Applications, 79: 164–80

GS Rao, PMK Prasad, DYV Prasad (2016). Performance Analysis of orthogonal and Biorthogonal wavelets for Edge detection of X-ray Images, Procedia Computer Science, 87:116-21.

Irem EK, Ayca Çi, Emine GS, Turgay I (2017). PCA based clustering for brain tumour segmentation of T1W MRI images, Computer Methods and Programs in Biomedicine, 140: 19-28.

Ivan C, Iker G (2017). MRI segmentation fusion for brain tumour detection, Information Fusion, 36: 1–9.

Kaur LS Gupta RC, Chauhan SCS (2007) Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets, Digital Signal Processing, 17: 189-98.

Kaveh Ahmadi, Ahmad YJ, Ezzatollah S (2015). An efficient compression scheme based on adaptive thresholding in wavelet domain using particle swarm optimization, Signal Processing: Image Communication, 32: 33–39.

Kennedy J, Eberhart RC (1995). A new Optimizer using Particle Swarm Theory, in Proceedings of Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 6: 39-43.

Lubna F, Adeel Y and Rana HR (2017). Adaptive stochastic segmentation via energy-convergence for brain tumour in MR image., Journal of Visual Communication and Image Representation, 46: 303–11.

Navas KA, Gayathri Devi KG, Athulya MS, Anjali V (2011). MWPSNR: A new image fidelity metric, IEEE Recent Advances in Intelligent Computational Systems(RAICS), 627-32.

Nooshin N, Miroslav K (2017). Automatic tumour segmentation in single-spectral MRI using a texture-based and contour-based algorithm, Expert Systems with Applications, 77: 1–10.

Passino KM (2002). Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine, 22: 52–67.

Prasanna S, Carrye W, Jerry Y (1997). Threshold Selection Using Renyi's Entropy, Pattern Recognition, 30(1): 71-84.

Prashant S, Ioana M (2003). Selective Thresholding in Wavelet Image Compression, Wavelets and Signal Processing Part of the series Applied and Numerical Harmonic Analysis, 2: 377-81.

Rajeswari R (2012). Type-2 Fuzzy Thresholded Bandlet Transform for Image Compression, Procedia Engineering, 38: 385-90.

Rajinikanth V, Suresh CS, Steven LF, Nachiappan S (2017). Entropy based segmentation of tumour from brain MR images – a study with teaching learning based optimization, Pattern Recognition Letters, 94: 87–95.

Ramakrishnan T, Sankaragomathi B (2017). A professional estimate on the computed tomography brain tumour images using SVM-SMO for classification and MRG-GWO for segmentation, Pattern Recognition Letters, 15:163–71.

Salleh. MFM, Soraghan J (2007). A New Multistage Lattice Vector Quantization with Adaptive Subband Thresholding for Image Compression, EURASIP Journal on Advances in Signal Processing December, 2: 23-34.

Sathya PD, Kayalvizhi R (2011). Optimal multilevel thresholding using bacterial foraging algorithm, Expert Systems with Applications, 38: 15549–64.

Sathya PD, Kayalvizhi. R (2011). Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images, Measurement, 44: 1828–48.

Siraj. S (2015). Comparative study of Birge–Massart strategy and unimodalthresholding for image compression using wavelet transform, Optik, 126: 5952-55.

Solmaz A, Farshad T (2017). Detection of brain tumour in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing, 219: 526–35.

Sudip Kumar A, Jamuna KS, Dipak K, Basu MN (2015). Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images, Applied Soft Computing, 34: 758-69.

Tahere IM, Mohammad RKM (2009). ECG Compression with Thresholding of 2-D Wavelet Transform Coefficients and Run Length Coding, European Journal of Scientific Research, 27: 248-57.

Tony FC, Zhou H (2007). Total Variation Wavelet Thresholding, Journal of Scientific Computing, 32(2): 315-41.

Yudong Z, Lenan W (2011). Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach, Entropy, 13(4): 841–59.

Yuhong L, Fucang J, Jing Q (2016). Brain tumour segmentation from multimodal magnetic resonance images via sparse representation, Artificial Intelligence in Medicine, 73: 1-13.

Downloads

Published

2018-12-06

How to Cite

Kumari, G. V., Sasibhushana Rao, G., & Prabhakara Rao, B. (2018). NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION. Image Analysis and Stereology, 37(3), 249–275. https://doi.org/10.5566/ias.1865

Issue

Vol. 37 No. 3 (2018)

Section

Original Research Paper

License

Copyright (c) 2018 Image Analysis & Stereology

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.