A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
محورهای موضوعی : Image ProcessingLeena Chandrashekar 1 , A Sreedevi Asundi 2
1 - Visvesvaraya Technological University
2 - Visvesvaraya Technological University
کلید واژه: Glioblastoma , Laplacian Pyramid , Image Fusion , Image Enhancement , Contrast Limited Adaptive Histogram Equalization , Particle Swarm Optimization,
چکیده مقاله :
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images.
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images.
[1] A.Prof Frank Gaillard et al. “Glioblastoma”. Article. https://radiopaedia.org/articles/glioblastoma.
[2] Javier E Villanueva, Marc C Mabray, Soonmee Cha, “Current Clinical Brain Tumor Imaging”, Neurosurgery 81:397-415, 2017.
[3] Olivier Keunen, Torfinn Taxt, et al., “Multimodal imaging of gliomas in context of evolving cellular and molecular therapies,” Adv. Drug Delivery Reviews, Vol.76, 2014, pp. 98-115.
[4] Wolf-Dieter Heiss, Peter Raab, Heinrich Lanferman, “Multimodality Assessment of Brain Tumors and Tumor Recurrence”, J Nucl Med 2011, Vol.52, pp.1585-1600.
[5] Maikel Verduin, Inge Compter, Danny Seijvers et al. “Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response”. Hindawi, Disease Markers, Vol. 2018, Article ID 2908609.
[6] Yin Fei, Gao Wei, Son Zongxi. “Medical Image Fusion Based on Feature Extraction and Sparse Representation”. Hindawi, International Journal of Biomedical Imaging, Vol. 2017.
[7] Hui Huang, Xi’an Feng, Jionghul Jiang. “Medical Image Fusion Algorithm Based on Nonlinear Approximation of Contourlet Transform and Regional Features”. Hindwai Journal of Electrical and Computer Engineering, Vol. 2017.
[8] Hiba Mzoughi, Ines Njeh, et al. “Histogram Equalization-Based Techniques for Contrast Enhancement of MRI Brain Glioma Tumor Images: Comparative Study,” in Proc. 4th.
[9] International Conference on Advanced Technologies for Signal and Image Processing, ATSIP, March 21-24, 2018.
[10] Bin Yang, Shutao Li, “Multifocus Image Fusion and Restoration with Sparse Restoration,” IEEE Transactions on Instrumentation and Measurement, Vol. 59, No.4, 2010.
[11] Robert D Fiete. “Modelling the Imaging Chain of Digital Camera”, Image Enhancement Processing, Chap. 9, [Online], SPIE Digital Library.
[12] Yakun Chang, Cheolkon Jung et al. “Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction”, IEEE Access, Vol. 6, 2010.
[13] Byong Seok Min, Dong Kyun Lim et al. “A Novel Method of Determining Parameters of CLAHE Based on Image Entropy”. International Journal of Software Engineering and Its Applications, Volume 7, No.5, pp. 113-120, 2013.
[14] Monika Agarwal and Rashima Mahajan. “Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization”, in Proc.6th International Conference on Smart Computing and Communication, ICSCC December 2017.
[15] Monika Agarwal and Rashima Mahajan. “Medical Image Contrast Enhancement using Quad Weighted Histogram Equalization with Adaptive Gama Correction and Homomorphic Filtering”, in Proc. 7th International Conference on Advances in Computing & Communications, ICACC-2017.
[16] Youlian Zhu, Cheng Huang. “An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping”, in Proc. 2012 International Conference on Solid State Devices and Materials Science, Physics Procedia 25, pp.621-628.
[17] K Zuiderveld. “Contrast Limited Adaptive Histogram Equalization”, in Graphics gems IV, pp474-485, San Diego, CA, USA, Academic Press Professional, Inc.
[18] Hardeep Kaur, Jyothi Rani. “MRI brain image enhancement using Histogram equalization techniques”, in Proc. IEEE WISPNET, 2016.
[19] Kitti Koonsanit, Saowapak Thongvigitmanee, et al. “Image Enhancement on Digital X-Ray Images using N-CLAHE”, in Proc. IEEE 10th Biomedical Engineering International Conference (BMEiCON), Aug 2017.
[20] Shelda Mohan and T R Mahesh. “Particle Swarm Optimization Based Contrast Limited Enhancement for Mammogram Images”, in Proc. 7th International Conference on Intelligent Systems and Control, 2013.
[21] Madhukar Bhat, Tarun Patil M S. “Adaptive Clip Limit for Contrast Limited Adaptive Histogram Equalization (CLAHE) of Medical Images using Least Mean Square Algorithm”, in Proc. International Conference on Advance Communication Control and Computing Technologies (ICACCCT), IEEE, 2014.
[22] Luis G More, Macrcos A Brizuela et al. “Parameter Tuning of CLAHE-based on Multi-Objective Optimization to Achieve Different Contrast Levels in Medical Images”, in Proc. International Conference on Image Processing (ICIP), IEEE 2015.
[23] Sheeba Jennifer, S Parasuraman, Amudha Kadirvelu. “Contrast Enhancement and Brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm”, Applied Soft Computing Vol. 42, pp.167-177, May 2016.
[24] Justin Joseph, J Sivaraman, R Periyasamy, V R Simi. “An Objective method to identify optimum clip-limit and histogram specification of contrast limited adaptive histogram equalization for MR Images,” Nalecz Institute of Biocybernetics and Biomedical Engineering 37, pp.489-497, 2017.
[25] Gurshan Singh, Anand Kumar Mittal. “Controlled Bilateral Filter and CLAHE Based Approach for Image Enhancement,” International Journal of Engineering and Computer Science, Volume 3, Issue 11, Nov 2014.
[26] Aziz Makandar, Bhagirathi Halalli. “Breast Cancer Image Enhancement using Median Filter and CLAHE,” in Proc. International Journal of Scientific & Engineering Research, Volume 6, Issue 4, 2015.
[27] Mehemet Zeki, Sarp Erturk. “Enhancement of Ultrasound Images with Bilateral Filter and Rayleigh CLAHE”, in Proc. 23rd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 2015.
[28] James Kennedy and Russell Eberhart. “Particle Swarm Optimization,” in Proc. International Conference on Neural Networks, 4, pp.1942-1948, IEEE 1995.
[29] Malik Braik, Alaa Sheta, Aladdin Ayesh. “Image Enhancement Using Particle Swarm Optimization,” Proceedings of World Congress on Engineering, Vol. 1, London, UK, 2007.
[30] Leena Chandrashekar, Sreedevi A. “Assessment of Non-Linear Filters for MRI Images,” in Proc. Second IEEE International Conference on Electrical, Computer and Communication Technologies, Feb 22-24, Coimbatore, India, 2017.
[31] Leena Chandrashekar, Sreedevi A. “A Hybrid Multimodal Medical Image Fusion Technique for CT and MRI brain images,” IGI Global International Journal of Computer Vision and Image Processing (IJCVIP), Vol. 8, Issue 3, Sept 2018.
[32] Leena Chandrashekar, Sreedevi A. “A Novel technique for fusing Multimodal and Multiresolution Brain Images,” in Proc. 7th International Conference on Advances in Computing and Communications, ICACC 2017, Aug 22-24, Cochin, India, 2017.
[33] Leena Chandrashekar, Sreedevi A, “Leena Chandrashekar, A Sreedevi, “A Multi Objective Enhancement Technique for Poor Contrast Magnetic Resonance Images of Brain Glioma blastoma”, Third International Conference on Computing and Network Communication, CoCoNet 2019.
