A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis
Keywords:biomedical image processing, fuzzy systems, image segmentation, fuzzy rules.
AbstractMedical imaging mainly manages and processes missing, ambiguous, omplementary,Â redundant and distorted data and information has a strong structural character. ThisÂ paper reports a new (semi)automated and supervised method for the segmentationÂ of brain structures using a rule-based fuzzy system. In the field of biomedical imageÂ analysis fuzzy logic acts as a unified framework for representing and processing bothÂ numerical and symbolic information, as well as structural information constitutedÂ mainly by spatial relationships. The developed application is for the segmentationÂ of brain structures in CT (computer tomography) images. Promising results showÂ the superiority of this knowledge-based approach over best traditional techniques inÂ terms of segmentation errors. The quantitative assessment of this method is madeÂ by comparing manually and automatic segmented brain structures by using someÂ indexes evaluating the accuracy of contour detection and spatial location. ThoughÂ the proposed methodology has been implemented and successfully used for modeldrivenÂ in medical imaging, it is general enough and may be applied to any imagisticÂ object that can be expressed by expert knowledge and morphological images.
Ahmed, M.N., et al (2002); A Modified Fuzzy C-means Algorithm for Bias Field Estimation and Segmentation of MRI Data, IEEE Trans. Medical Imaging, 21(3): 193-199. http://dx.doi.org/10.1109/42.996338
Algorri, M.-E.; Flores-Mangas, F.; Classification of Anatomical Structures in MR Brain Images using Fuzzy Parameters, IEEE Trans. Biomedical Eng., 51(9): 1599-1608, 2004. http://dx.doi.org/10.1109/TBME.2004.827532
Batenburg, K.J.; Sijbers, J.; Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization, IEEE Trans. Medical Imaging, 28(5): 676 - 686, 2009. http://dx.doi.org/10.1109/TMI.2008.2010437
Bezdek, J.C. et al.; Fuzzy Models and Algorithms for Pattern Recognition and Image Processing (The Handbook of Fuzzy Sets), Springer, Berlin, 2005.
Boskovitz, V.; Guterman, H.; An Adaptive Neuro-Fuzzy System for Automatic Image Segmentation and Edge Detection, IEEE Trans. Fuzzy Systems, 10(2): 247-262, 2002. http://dx.doi.org/10.1109/91.995125
Brejl, M.; Sonka, M.; Object Localization and Border Detection Criteria Design in Edge- Based Image Segmentation. Automated Learning from Examples, IEEE Trans. Med. Imaging, 19: 973-985, 2000. http://dx.doi.org/10.1109/42.887613
Costin, H.; Biomedical Image Processing and Analysis via Artificial Intelligence and Information Fusion, in: Ichimura, T., Yoshida, K. (Eds.), Knowledge Based Intelligent Systems for Health Care, Advanced Knowledge Int. Publ. House, Australia, 121-160, 2004.
Costin, H.; Rotariu, C.; Medical Image Analysis and Representation using a Fuzzy and Rule- Based Hybrid Approach, Int J Comput Commun, 1(S):156-162, 2006.
Etienne, E.K.; Nachtegael, M. (Eds.); Fuzzy Techniques in Image Processing, Physica-Verlag, N.Y., 2000.
Farag, A.A.; El-Baz, A.S.; Gimel'farb, G.; Precise Segmentation of Multimodal Images, IEEE Trans. Image Processing, 15(4): 952-968, 2006. http://dx.doi.org/10.1109/TIP.2005.863949
Gonzales, R.C.; Woods, R.E.; Digital Image Processing, 2nd Ed., Prentice Hall, New Jersey, 2001.
Hung, W.-L.; Yang, M.-S.; Chen, D.-H.; Parameter Selection for Suppressed Fuzzy C-Means with an Application to MRI Segmentation, Pattern Recognition Letters, 27(5): 424-438, 2006. http://dx.doi.org/10.1016/j.patrec.2005.09.005
Jimenez-Alaniz, J.R.; Medina-Banuelos, V.; Yanez-Suarez, O.; Data-Driven Brain MRI Segmentation Supported on Edge Confidence and A Priori Tissue Information, IEEE Trans. Medical Imaging, 25(1): 74-83, 2006. http://dx.doi.org/10.1109/TMI.2005.860999
Liao, P.-S.; Chen, T.-S.; Chung, P.-C.; A Fast Algorithm for Multilevel Thresholding, Journal of Information Science and Engineering, 17: 713-727, 2001.
Ma, L.; Staunton, R.C.; A Modified Fuzzy C-Means Image Segmentation Algorithm for Use with Uneven Illumination Patterns, Pattern Recognition, 40(11): 3005-3011, 2007. http://dx.doi.org/10.1016/j.patcog.2007.02.005
Moon, T.K.; More Mathematical Methods and Algorithms for Signal Processing, Utah State University, 2000.
Otsu, N.; A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. SMC, 9(1): 62-66, 1979.
Rangayyan, R.M.; Biomedical Image Analysis, CRC Press, Boca Raton, FL, 2005.
Semmlow, J.L.; Biosignal and Biomedical Image Processing MATLAB-Based Applications, M. Dekker, 2004. http://dx.doi.org/10.1201/9780203024058
Sharma, N.; Aggarwal, J.M.; Automated Medical Image Segmentation Techniques, Journal of Medical Physics, 35(1): 3-14, 2010. http://dx.doi.org/10.4103/0971-6203.58777
Tabakov, M.; A Fuzzy Segmentation Method for Computed Tomography Images, Int. J. Intellig. Inform. Database Syst. Technol. Appl., 1: 234-246, 2007.
Zhou, J.; Rajapakse, J.C.; Segmentation of Subcortical Brain Structures Using Fuzzy Templates, Neuroimage, 28: 915-924, 2005. http://dx.doi.org/10.1016/j.neuroimage.2005.06.037
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