A Novel Skull Stripping and Enhancement Algorithm for the Improved Brain Tumor Segmentation using Mathematical Morphology

A Novel Skull Stripping and Enhancement Algorithm for the Improved Brain Tumor Segmentation using Mathematical Morphology

Автор: Benson C. C., Lajish V. L., Kumar Rajamani

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 7 vol.8, 2016 года.

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Human brain is a complex system, made up of neurons and glial cells. Nothing in the universe can compare with the functioning of human brain. Due to its complex nature, the diseases affected on the brain is also very complex in nature. Brain imaging is the widely used method for the diagnosing of such deceases. Brain tumor is an abnormal mass of tissue in which cells grow and multiply uncontrollably, seemingly unchecked by the mechanisms that control normal cells. Magnetic Resonance Imaging (MRI) is a commonly used modality for detecting the brain diseases. In this work we proposed a novel method for the preprocessing of MR brain images for the improved segmentation of brain tumor based on mathematical morphology operations. The first part of this paper proposes an efficient method for the skull stripping of brain MR images based on mathematical morphology. One of the main disadvantages of MRI technology is its low contrast. The second part of this paper implements an algorithm for the contrast enhancement of MR brain images using morphological operations. The output of this algorithms are evaluated using standard measures. The experimental part shows that the proposed method produces very prominent and efficient results.

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Contrast Enhancement, Skull Stripping, Mathematical Morphology, Erosion, Dilation

Короткий адрес: https://sciup.org/15013997

IDR: 15013997

Список литературы A Novel Skull Stripping and Enhancement Algorithm for the Improved Brain Tumor Segmentation using Mathematical Morphology

  • Danielle S. Bassett and Michael S. Gazzaniga, "Understanding complexity in the human brain", Trends in Cognitive Sciences, 15(5), 2011.
  • Alan Wee-Chung Liew and Hong Yan, "Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images", Current Medical Imaging Reviews, 2 (1), 2006.
  • W.L. Nowinski, "Biomechanics of the Brain" Biological and Medical Physics, Biomedical Engineering", K. Miller (ed.), Chapter 2, 2011.
  • S. Cha, "Update on Brain Tumor Imaging: From Anatomy to Physiology", American Journal of Neuroradiology, 27, 2006, 475-487.
  • https://explorable.com/wilhelm-conrad-roentgen, Wilhelm Conrad Roentgen and the Discovery of X-Ray Beams.
  • P.Narendran, V. K. Narendira Kumar and K. Somasundaram, "3D Brain Tumors and Internal Brain Structures Segmentation in MR Images", International Journal of Image, Graphics and Signal Processing, 2012, 1, 35-43.
  • Horst K. Hahn and Heinz-Otto Peitgen, "The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform", Proc. MICCAI, LNCS, 1935, 2000, 134-143, Springer.
  • Shafaf Ibrahim, Noor Elaiza Abdul Khalid, Mazani Manaf and Mohd Ezane Aziz, "Qualitative Analysis of Skull Stripping Accuracy for MRI Brain Images", Ubiquitous Information Technologies and Applications, Lecture Notes in Electrical Engineering, 2013.
  • Sonia Goyal and Seema Baghla, "Region Growing Adaptive Contrast Enhancement of Medical MRI Images", Journal of Global Research in Computer Science, 2(7), 2011.
  • F. Segonne, A.M. Dale, E. Busa, M. Glessner, D. Salat, H.K. Hahn, and B. Fischl, "A hybrid approach to the skull stripping problem in MRI", NeuroImage, 22, 2004, 1060– 1075.
  • Andre G.R. Balan, AgmaJ.M.Traina, Marcela X. Ribeiro, Paulo M. A. Marques and Caetano Traina Jr., "Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI", Computers in Biology and Medicine, 42, 2012, 509-522.
  • Juan Eugenio Iglesias, Cheng-Yi Liu, Paul Thompson and Zhuowen Tu, "Robust Brain Extraction across Datasets and Comparison with Publicly Available Methods", IEEE Transactions on Medical Imaging, 2011.
  • Francisco J. Galdames, Fabrice Jaillet and Claudio A. Perez, "An Accurate Skull Stripping Method Based on Simplex Meshes and Histogram Analysis in Magnetic Resonance Images", Elsevier, Journal of Neuroscience Methods, 206, 2012, 103–119.
  • Audrey H. Zhuang, Daniel J. Valentino and Arthur W. Toga, "Skull-stripping Magnetic Resonance Brain Images Using a Model-based Level Set", Neuro Image, 32, 2006, 79-92.
  • Orazio Gambino, Enrico Daidone, Matteo Sciortino, Roberto Pirrone and Edoardo Ardizzone, "Automatic Skull Stripping in MRI based on Morphological Filters and Fuzzy C-means Segmentation", 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, 2011.
  • Dwarikanath Mahapatra, "Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts", Springer, J Digit Imaging, 25, 2012, 802–814.
  • Rosniza Roslan, Nursuriati Jamil and Rozi Mahmud, "Skull Stripping of MRI Brain Images using Mathematical Morphology", 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), 2010.
  • John Chivertona, Kevin Wells, Emma Lewis, Chao Chen, Barbara Podda and Declan Johnson, "Statistical Morphological Skull Stripping of Adult and Infant MRI Data", Computers in Biology and Medicine, 37, 2007, 342-357.
  • Selvaraj. D and Dhanasekaran. R. "MRI Brain Tumor Detection by Histogram and Segmentation by Modified GVF Model", International Journal of Electronics and Communication Engineering & Technology (IJECET), 4(1), 2013, 55-68.
  • Sajjad Mohsin, Sadaf Sajjad, Zeeshan Malik, and Abdul Hanan Abdullah, "Efficient Way of Skull Stripping in MRI to Detect Brain Tumor by Applying Morphological Operations, after Detection of False Background", International Journal of Information and Education Technology, 2(4), 2012.
  • Maher un Nisa and Ahsan Khawaja, "Contrast Enhancement Impact on Detection of Tumor in Brain MRI", Science International, (Lahore), 27(3), 2015, 2161-2163.
  • R. C. Gonzales and R. E. Woods, "Digital Image processing", Third Edition, Prentice Hall, 2008.
  • Pratik Vinayak Oak and Prof. Mrs. R. S. Kamathe, "Contrast Enhancement of Brain MRI Images using Histogram based Techniques", International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 1(3), 2013.
  • Y. T. Kim, "Contrast Enhancement Using Brightness Preserving Bi-Histogram Equation", IEEE Transactions on Consumer Electronics, 43(1), 1997, 1-8.
  • Ali Ziaei, Hojatollah Yeganeh, Karim Faez and Saman Sargolzaei, "A Novel Approach for Contrast Enhancement in Biomedical Images Based on Histogram Equalization", IEEE International Conference on Bio-Medical Engineering and Informatics, 2008.
  • D. J. Ketcham, R. W. Lowe and J. W. Weber, "Image enhancement techniques for cockpit displays", Tech. rep., Hughes Aircraft, 1974.
  • A. Boschetti, N. Adami, R. Leonardi and M. Okuda, "High Dynamic Range Image Tone Mapping Based On Local Histogram Equalization", IEEE, 2010.
  • N Senthilkumaran and J Thimmiaraja, "A Study on Histogram Equalization for MRI Brain Image Enhancement", Proc. of Int. Conf. on Recent Trends in Signal Processing, Image Processing and VLSI, Association of Computer Electronics and Electrical Engineers, 2014.
  • S.-D. Chen and A. R. Ramli, "Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation", IEEE Transactions on Consumer Electronics, 49(4), 2003), 1301-1309.
  • H. Ibrahim and N. S. Pik Kong, "Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement", IEEE Transactions on Consumer Electronics, 53(4), 2007, 1752-1758.
  • Y. Wang, Q. Chen and B. Zhang, "Image Enhancement Based on Equal Area Dualistic Sub-Image Histogram Equalization Method", IEEE Transactions on Consumer Electronics, 45(1), 1999), 68-75.
  • S.-D. Chen and A. R. Ramli, "Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement", IEEE Transactions on Consumer Electronics, 49(4), 2003, 1310-1319.
  • A. Djerouni, H. Hamada, and N. Berrached, "MR imaging contrast enhancement and segmentation using fuzzy clustering", IJCSI International Journal of Computer Science Issues, 8(4), 2011.
  • R. C. Gonzales and R. E. Woods, "Digital Image processing", Chapter 9, Third Edition, Prentice Hall, 2008.
  • Jean. Serra, "Image Analysis and Mathematical Morphology", Academic Press, 1982.
  • N. Otsu's, "A threshold selection method from gray level histograms", IEEE Transactions on systems, MAN and Cybernetics, 9(1), 1979.
  • Lee R. Dice, "Measures of the Amount of Ecologic Association between Species", Ecological Society of America, 26(3), 1945, 297–302.
  • Jaccard, P.: The Distribution of Flora in Alpine Zone, New Phytol, 11(2), 1912, 37-50.
  • P.A. Yushkevich, J. Piven, H. Cody, S. Ho, J.C. Gee, G. Gerig, "User-guided level set segmentation of anatomical structures with ITK-SNAP", Insight J., 1, 2005.
  • Keun Jo Jang, Dae Cheol Kweon, Jong-Woong Lee, Jiwon Choi, Eun-Hoe Goo, Kyung-Rae Dong, Jae-Seung Lee, Gye Hwan Jin and Sungbo Seo, "Measurement of Image Quality in CT Images Reconstructed with Different Kernels", Journal of the Korean Physical Society, 58(2), 2011, 334-342.
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