A Review of Intensity and Model Based Segmentation Methods for MRI

R. Neela*, R. Kalaimagal**
* Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, India.
** Assistant Professor, Department of Computer Science, Govt. Arts College for Men (Autonomous), Chennai, India.
Periodicity:July - September'2014
DOI : https://doi.org/10.26634/jip.1.3.2958

Abstract

Segmentation of various structures is a crucial step in the diagnosis and treatment of various diseases. Various imaging techniques such as X-ray, CT, (Computer Tomography), DTI (Diffusion Tensor Image), MRI (Magnetic Resonance Imaging), FMRI (Functional Magnetic Resource Imaging), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), etc. are used in the treatment of diseases. Selection of the segmentation method depends on the modality used and the structures to be segmented. Accurate segmentation of various structures and computation of volume of tissues are required in the treatment of various diseases. In this paper, the authors present an overview of various segmentation techniques used in MRI analysis and their pros and cons. Also, the performance measures of some methods are evaluated.

Keywords

Keywords: MRI Segmentation, Automatic Segmentation, Intensity Based and Model Based.

How to Cite this Article?

Neela, R., and Kalaimagal, R. (2014). A Review of Intensity and Model Based Segmentation Methods for MRI. i-manager’s Journal on Image Processing, 1(3), 1-10. https://doi.org/10.26634/jip.1.3.2958

References

[1]. Neeraj Sharma, and Lalit M. Aggarwal. (2010). Automated medical image segmentation techniques. J Med Phys. Vol.35, No.1, pp3–14. doi: 10.4103/0971- 6203.58777 PMCID: PMC2825001.
[2]. Balafar, Mohd Ali, Abdul Rahman Ramli, Iqbal Saripan, M., and Syamsiah Mashohor (2010). “Review of brain MRI image segmentation methods”. Artificial Intelligence Review Vol.33(3), pp.261-274. Retrieved from http://www.via.cornell.edu/ec e578/project/2014/g1/paper8.pdf.
[3]. Mehmet Sezgin, & Bulent Sankur. (2004). “Survey over image thresholding techniques and quantitative performance evaluation”. Journal of Electronic Imaging, Vol.13(1), pp.146–165.
[4]. Lee, S.U., Chung, S.Y., and Park, R.H. (1990). “A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation”. Graphical Models and Image Processing, Vol 52, pp.171-190.
[5]. Weszka, J.S., and Rosenfeld, A. (1978). “Threshold evaluation techniques”. IEEE Trans. Systems, Man and Cybernetics, SMC-Vol.8(8), pp.627-629.
[6]. Sahoo, P.K., Soltani, S., and Wong, A.K.C. (1998). “A survey of thresholding techniques”. Computer Vision, Graphics, and Image Processing, Vol. 41, pp.233-260.
[7]. Salem Saleh Al-amri, Kalyankar, N.V., and Khamitkar S.D. (May 2010). “Image Segmentation by Using Threshold Techniques”. Journal of Computing, Vol.2(5). ISSN 2151-9617.
[8]. Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, pp.641-647.
[9]. Mehnert, & Jackway, P. (1997). “An improved seeded region growing algorithm”. Pattern Recognition Letters, Vol.18, pp.1065-1071.
[10]. Manousakas N., Undrill PE., Cameron G.G., et al. (1998). “Split-and-merge segmentation of magnetic resonance medical images: Performance evaluation and extension to three dimensions”. Comp Biomed Res, Vol .31: pp.393–412.
[11]. Judith MS Prewitt. (1970). “Object enhancement and extraction”, Volume 75. Academic Press, New York.
[12]. John Canny. (1986). “A computational approach to edge detection”. IEEE Transactions on, Pattern Analysis and Machine Intelligence, PAMI-8(6), pp.679–698.
[13]. BoyKob, Y.,and Funka Lea, G. (2006). “Graph cuts and efficient n-d image segmentation”. International Journal of Computer Vision, Vol. 69 No(1), pp.109-131.
[14]. Li, S. Z. (1995). “Markov Random Field Modeling in Computer Vision”. Berlin, Germany: Springer-Verlag.
[15]. Wells III, W. M., Grimson,W.E.L., Kikinis,R., and Jolesz, F.A. (1996). Adaptive Seg mentation of MRI data. IEEE Trans. Med. Imag., Vol.15, 429-442.
[16]. Pappas, T.N. (1992). “An Adaptive Clustering Algorithm for Image Segmentation”. IEEE Trans. Signal Proc., Vol.40, pp.901-914.
[17]. Kass, M., Witkin, A., and Terzopolous, D. (1987). “ Snakes: Active Contour models. Proceedings, International Conference on Computer Vision”, IEEE Computing Society Press.
[18]. Cohen, L., & Cohen, I. (1993). “Finite element methods for active contour models and balloons for 2-D and 3-D images”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.15(11), pp.1131-1147. Retrieved from https:// www.ceremade.dauphine.fr /~cohen/mypapers/CohenCohenPAMI93.pdf.
[19]. Ronfard, R. (1994). “Region-based strategies for active contour models”. Int. J. Comput. Vis. Vol.13(2), pp.229–251.
[20]. Zhu, S.C., and Yuille, A. (1996). “Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.18(9), pp.884–900.
[21]. Caselles, V, Catte, F, Coll, T. & Dibos, F. (1993). “A geometric model for active contours”. Numerische Mathematik, Vol.66(1), pp.1–31. Retrieved from http://www.dtic.upf.edu/~vcaselles/papers_v/Geometri MACConf.pdf.
[22]. Malladi, R., Sethian, J.A., & Vemuri, B.C. (1995). “Shape modeling with front propagation: a level set approach”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.17(2), pp.158– 175. Retrieved from http://homes.cs.washington.edu/~seitz/course/590SS/p ami_fronts.pdf.
[23]. Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., & Lorenz, C. (2009). “Automated model-based vertebra detection, identification, and segmentation in CT images”. Medical Image Analysis, Vol.13(1), pp.471-8.
[24]. Poon, C.S., & Braun M. (1997). “Image segmentation by a deformable contour model incorporating region analysis”. Physics Medicine and Biology, Vol.42 (9), pp.1833-41. doi:10.1088/0031-9155/42/9/013.
[25]. Xu,, C., Pham, D.L., and Prince, J. L. (2000). “Image Segmentation Using Deformable Models”. Handbook of Medical Imaging, SPIE Press, Vol.2(1),pp. 447 – 514.
[26]. Cootes, T. F., Hill, A., Taylor, C.J., and Haslam, J. (1994). “Use of active shape models for locating structures in medical images”. Image and Vision Computing, Vol.12(6), pp.355 -366. Retrieved from http://www.sci.utah.edu/~gerig/CS7960-S2010/handouts/ivc95.pdf.
[27]. Cootes, T.F., Taylor, C.J., Cooper, D.H., and Graham, J. (1995). “Active shape models–their training and application”.ComputerVisionandImage Understanding, Vol.61(1), pp.38-59. Retrieved from http://www.inf.unideb.hu/~sajolevente/papers/aam/199 5.%20cviu95%20-%20active%20shape%20models. Pdf
[28]. Xian Fan, Yiqiang Zhan and Gerardo Hermosillo Valadez. (2009). “A Comparison study of atlas based image segmentation and the advantage of multi-atlas based on shape clustering”. Proc. SPIE 7259, Medical Imaging. doi:10.1117/12.814157.
[29]. Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J.V., and Rueckert, D. (2009). “Multi-Atlas Based Segmentation of Brain Images: Atlas Selection and Its Effect on Accuracy”. NeuroImage, Vol.46(3), pp.726- 739.Retrived from http://www.doc.ic.ac.uk/~pa100/pubs/ aljabarNeuroImage 2009-selection.pdf.
[30]. Rohlfing, T., Robert Brandt, Randolf Menzel,and Calvin R. Maurer, Jr. (2004). “Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains ”. NeuroImage, 21(4), 1428–1442, doi: 10.1016/j.neuroimage.2003.11.010.
[31]. Zitova, B., and Flusser, J. (2003). “Image registration methods: a survey”. Image and Vision Computing, Vol.21(11), pp.977–1000.
[32]. Warfield, S. K., Zou, K.H., and Wells, W.M. (2004). “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation”. IEEE Transactions on Medical Imaging, Vol.23(7), pp. 903–921.
[33]. Rohlfing and C. R. Maurer Jr. (2007). “Shape-based averaging”, IEEE Transactions on Image Processing, Vol.16(1), pp.153–161.
[34]. M. R. Sabuncu, B. T. T. Yeo, K. Van Leemput, B. Fischl, and P. Golland, “A generative model for image segmentation based on label fusion,” IEEE Transactions on Medical Imaging, Vol. 29, No. 10, Article ID 5487420, pp. 1714–1729, 2010.
[35]. Hongzhi Wang, J., Suh, W., Das, S.R., Pluta, J.B., Craige, C., and Yushkevich, P.A. (2012). “Multi-Atlas Segmentation with Joint Label Fusion”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 611- 623. doi:10.1109/TPAMI.2012.143.
[36]. http://placid.nlm.nih.gov/user/48/
[37]. Advanced Normalization Tools (ANTs) [Online]. Available : http://sourceforge.net/projects/advants/.
[38]. Ruben Cardenes, Meritxell Bach, Ying-Veronica Chi, Ioannis Marras, Rodrigo de Luis, Mats Anderson, Peter Cashman and Matthieu Bultelle. (2007). “Multimodal Evaluation Method for Medical Image Segmentation”. Computer Analysis of Images and Patterns, 4673, pp 229-236.
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