A Fuzzy Rules-Based Segmentation Method for Medical Images Analysis

Authors

  • Hariton Costin 1. Faculty of Medical Bioengineering, Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania; M. Kogalniceanu Str., No. 9-13, 700454, Iasi, Romania; 2. Institute of Computer Science of Romanian Academy, Iasi Branch, Carol I Blvd., No. 11, 700506, Iasi

Keywords:

biomedical image processing, fuzzy systems, image segmentation, fuzzy rules.

Abstract

Medical imaging mainly manages and processes missing, ambiguous, omplementary, redundant and distorted data and information has a strong structural character. This paper reports a new (semi)automated and supervised method for the segmentation of brain structures using a rule-based fuzzy system. In the field of biomedical image analysis fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information, as well as structural information constituted mainly by spatial relationships. The developed application is for the segmentation of brain structures in CT (computer tomography) images. Promising results show the superiority of this knowledge-based approach over best traditional techniques in terms of segmentation errors. The quantitative assessment of this method is made by comparing manually and automatic segmented brain structures by using some indexes evaluating the accuracy of contour detection and spatial location. Though the proposed methodology has been implemented and successfully used for modeldriven in medical imaging, it is general enough and may be applied to any imagistic object that can be expressed by expert knowledge and morphological images.

References

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

Published

2013-02-18

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.