Abstract
In this paper, we present an automated SVM segmentation scheme for the MR images for the early diagnosis of neurodegenerative diseases. This method consists of three steps. In the first method we undergo the preprocessing part which removes the skull and the unwanted areas, and then the features are extracted. The third step, the multiple SVM is used to segment the MR images into Gray Matter (GM), White Matter (WM), and Cerebro Spinal Fluid (CSF). The SVM technique is a powerful discriminator is able to handle nonlinear classification problems. The proposed method is used to segment GM, WM and CSF from real magnetic resonance imaging (MRI) in south Indian population. The automated segmented brain tissues are then evaluated by comparing it with the corresponding ground truth set by the radiologist.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bezdek JC, Hall LO, Clarke LP (1993) Review of MR image segmentation techniques using pattern recognition. Med Phys 20:1033–1048
Held K, Kops ER, Krause BJ, Wells WM III, Kikinis R, Muller Gartner HW (1997) Markov random field segmentation of brain MR images. IEEE Trans Med Imaging 16:878–886
Liew AW, Yan H (2009) An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans Med
Wells WM, Grimson WE, Kikinis R, Jolesz FA (1996) Adaptive segmentation of MRI data. IEEE Trans Med Imaging 15:429–442
Li C, Kao CY, Gore JC, Ding Z (1996) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process C 17:1940–1949
Wang L, Chen Y, Pan X, Hong X, Xia D (2010) Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy. J Neurosci Methods 188:316–325
Collins DL, Holmes CJ, Peters TM, Evans AC (1995) Automatic 3D model-based neuroanatomical segmentation. Hum Brain Mapp C 3:190–208
Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3:672–682
Wu Y, Guo L (2006) Multi-layer support vector machine and its application. In: International conference IEEE transactions on machine learning and cybernetics, pp 3627–3631
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge, UK
Osuna E, Freund R, Girosi F (1997) Training support vector machines: application to face detection. In: Proceeding computer vision and pattern recognition conference proceedings. IEEE Computer Society, pp 130–136
Pontil M, Verri A (1998) Support vector machines for 3D object recognition. IEEE Trans Pattern Anal Mach Intell 20:637–646
Scholkopf B, Kah-Kay S, Burges CJ, Girosi F, Niyogi P, Poggio T et al (1997) Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45:2758–2765
Guo L, Liu X, Wu Y, Yan W, Shen X (2007) Research on the segmentation of MRI image based on multi-classification support vector machine. Conf Proc IEEE Eng Med Biol Soc 6020–6023
Deepa N, Aruna Devi B (2011) A survey on artificial intelligence approaches for medical image classification. Indian J Sci Technol 4(11):1583–1595
Desikan SR, Howard JC (2009) Automated MRI measures identify individuals with MCI and AD. Brain 132:2048–2057
Dheeba J (2012) Tamil Selvi: an improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network. J Med Syst 36(5):3223–3232
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Paul, G., Varghese, T., Purushothaman, K.V., Singh, A. (2015). Automated Segmentation of MR Images by Implementing Multi SVM Technique. In: Kamalakannan, C., Suresh, L., Dash, S., Panigrahi, B. (eds) Power Electronics and Renewable Energy Systems. Lecture Notes in Electrical Engineering, vol 326. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2119-7_147
Download citation
DOI: https://doi.org/10.1007/978-81-322-2119-7_147
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2118-0
Online ISBN: 978-81-322-2119-7
eBook Packages: EngineeringEngineering (R0)