A Comparative Based Review on Image Segmentation of Medical Image and its Technique
Keywords:
Fuzzy C Means (FCFS), MRI, Region based segmentation, Line detectionAbstract
This paper presents a survey on Image segmentation. In Image segmentation dividing an image into many regions is the segmentation process. Segmentation Process provides a way to find a particular region of point inside an image. This process provides help in understanding the process in a meaningful way. In this paper, a survey of various techniques of image segmentation their algorithm that helps in finding Medical images.
References
P. Ranjan, P.R. Khan, "Wavelet approximated texture data watershed transform (WATDWT) segmentation of Bio-Medical Images", International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.26-31, 2017.
P. Ranjan, P.R. Khan, "Review of improved A.I. based Image Segmentation for medical diagnosis applications", International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.75-81, 2016.
J. Rahebi, Z. Elmi, A. Farzam, K. Shayan, "Digital image edge detection using an ant colony optimization based on genetic algorithm”, 2010 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp.145-149, 2010.
Karanbir singh and Ashima Kalra, "Improving MRI Segmentation by Fuzzy C Mean Clustering Algorithm Using BBHE Techniques", International Journal of Computer Sciences and Engineering, Vol.03, Issue.05, pp-143-147, 2015.
A. Halder, A. Dasgupta, S. Ghosh, "Image segmentation using rough-fuzzy K-medoid algorithm", 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS), Kolkata, pp.105-108, 2012.
H. T. T. Binh, M. D. Loi and N. T. Thuy, "Improving Image Segmentation Using Genetic Algorithm", 11th International Conference on Machine Learning and Applications, Boca Raton, pp.18-23, 2012.
J. Bezdek. L. Hall. and L. Clarke, “Review of MR image segmentation using pattern recognition”, Medical Physics, vol. 20, pp.1033–48, 1993.
J. K. Udupa, L. Wei, S. Samarasekera, Y. Miki, M.A. van Buchem, R.I. Grossman, "Multiple sclerosis lesion quantification using fuzzy-connectedness principles", IEEE Transactions on Medical Imaging. vol. 16, pp. 598-609, 1997.
D.L. Pham, “Unsupervised Tissue Classification in Medical Images using Edge-Adaptive Clustering”, Proceedings of the 25th Annual International Conference of the IEEE EMBS. Cancun. Mexico, pp.17-21. 2003.
L. Jiang, W. Yang, “A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images”, Proc. VIIth Digital Image Computing: Techniques and Applications, Sydney, pp.10-12, 2003.
D.L. Pham, J.l. Prince, “Adaptive fuzzy segmentation of magnetic resonance images”, IEEE Transaction in Medical Imaging, Vol. 18. pp. 737–752, 1999.
C. Xu, D.L Pham, J.L. Prince, “Finding the brain cortex using fuzzy segmentation. isosurfaces. and deformable surfaces”,International Conference on Inform. Processing in Medical Imaging, UK, pp. 399-404, 1997.
S.R. Kannan, “Segmentation of MRI Using New Unsupervised Fuzzy C Mean Algorithm”, ICGST-GVIP Journal. Vol. 5, No.2, 2005.
S. Alizadeh, M. Ghazanfari, M. Fathian, “Using Data Mining for Learning and Clustering FCM”, International Journal of Computational Intelligence. Vol. 4, No. 2, 2008.
A. Wee, C. Liew, and H. Yan, “Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images”, Current Medical Imaging Reviews, Vol. 2, No. 1, pp. 1-13, 2006.
M.W. Hansen and W.E. Higgins, “Relaxation Methods for Supervised Image Segmentation”, IEEE Trans. on Pattern Analysis and Machine Intelligence. Vol. 19, No. 9, 1997.
M.N. Ahmed and A.A. Farag, “Two stages Neural Network for Medical Volume Segmentation”, Pattern Recognition Letters, Vol.18, Issues11, pp.1143–1151, 1997.
L.O. Hall, A.M. Bensaid, L.P. Clarke, R.P. Velthuizen, M.S. Silbger, J.C. Bezdek, “A Comparison of Neural Network and Fuzzy Clustering Techniques in Segmenting Magnetic Resonance Images of the Brain”, IEEE Transactions on Neural Networks. Vol. 3, No.5, pp. 672-681, 1992.
K.S. Chuang, H.L, Tzeng, S. Chen, J. Wu, T.J. Chen, “Fuzzy C-Means Clustering with Spatial Information for Image Segmentation”, Computerized Medical Imaging and Graphics”, Vol. 30, pp. 9–1, 2006.
Y. Yang, S. Huang, “Image Segmentation By Fuzzy C- Means Clustering Algorithm With A Novel Penalty Term”,Computing and Informatics, vol. 26, pp. 17-31, 2007.
J.C. Dunn, “A Fuzzy Relative of the ISODATA Process and its Use in Detection Compact Well Separated Clusters”, Journal of Cybernetics, Vol. 3, pp. 32-57, 1974.
J.C. Bezdec, “Pattern Recognition with Fuzzy Objective Function Algorithms”, Plenum Press, New York, pp.15-82, 1981.
Alan Wee-Chung, Liew and Hong Yan, (2006), “Current Methods in the Automatic Tissue Segmentation of 3D Magnetic Resonance Brain Images”, Current Medical Imaging Reviews, Vol. 2, No. 1, pp. 1-13.
X. Zhang, L. Yin, J.F. Cohn , S. Canavan , M. Reale , A. Horowitz , P. Liu, J.M. Girard, “BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database”, Image and Vision Computing, Vol.32, Issue.10, pp. 692–706, 2014.
H. Liu, L. Li, C. Wu, “Color Image Segmentation Algorithms based on Granular Computing Clustering”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7, Issue.2, pp.155-168, 2014.
H. Liang, A. Lucian, R. Lange , C.S. Cheung, B. Su, “Remote spectral imaging with simultaneous extraction of 3D topography for historical wall paintings”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.95, pp.13-22, 2014.
A. Roebroeck , K. Uludağ, “General overview on the merits of multimodal neuroimaging data fusion”, Neuro Image, Vol.102, Issue.1, pp.3–10, 2014.
C. Bhuvaneswari , P. Aruna , D. Loganathan, “A new fusion model for classification of the lung diseases using genetic algorithm”, Egyptian Informatics Journal, Vol.15, Issue.2, pp.69–77, 2014.
B. Kaur, P. Kaur, "A Comparative Study on Image Segmentation Techniques", International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.50-56, 2015.
B. J. Zwaag, K. Slump, and L. Spaanenburg, “Analysis of neural networks for edge detection”, 13th Workshop on Circuits, Systems and Signal Processing, Netherlands, pp. 580-586, 2002.
I. Irum, M. Raza, M. Sharif, “Morphological techniques for medical images: A review”,Research Journal of Applied Sciences, Engineering and Technology, Vol.4, Issue.17, pp.2948-2962, 2012.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
