Unsupervised Segmentation using Fuzzy Logicbased Texture Spectrum for MRI Brain Images
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32804
Unsupervised Segmentation using Fuzzy Logicbased Texture Spectrum for MRI Brain Images

Authors: G.Wiselin Jiji, L.Ganesan

Abstract:

Textures are replications, symmetries and combinations of various basic patterns, usually with some random variation one of the gray-level statistics. This article proposes a new approach to Segment texture images. The proposed approach proceeds in 2 stages. First, in this method, local texture information of a pixel is obtained by fuzzy texture unit and global texture information of an image is obtained by fuzzy texture spectrum. The purpose of this paper is to demonstrate the usefulness of fuzzy texture spectrum for texture Segmentation. The 2nd Stage of the method is devoted to a decision process, applying a global analysis followed by a fine segmentation, which is only focused on ambiguous points. The above Proposed approach was applied to brain image to identify the components of brain in turn, used to locate the brain tumor and its Growth rate.

Keywords: Fuzzy Texture Unit, Fuzzy Texture Spectrum, andPattern Recognition, segmentation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061449

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1654

References:


[1] Rosenfield and A.C.Kak," Digital image processing", 2nd edition.
[2] L.N., Wang F.Z., Mapps D.J., Robinson P., Jenkins D., Clegg W.W., "Nano-scale positioning for magnetic recording". European Journal of Sensors and Actuators, Vol. A81, Nos. 1-3, April 2000, pp 313-316.
[3] F. Liu and R. W. Picard. Periodicity, directionality, and randomness: Wold features for image modeling and retrieval. Technical report, MIT Media Laboratory and Modeling Group, technical Report No.320, 1994.
[4] S.-F. Chang. Compressed-domain techniques for image/video indexing and manipulation. In International Conference on Image Processing, Special Session on Digital Library and Video-On Demand. I.E.E.E., October 1995.
[5] Wang,"A new statistical approach for texture analysis".
[6] P.Brodatz texture -A photographic Album for arucs and designers, renhold.
[7] Shifeng Weng, Changshui Zhang and Zhonglin Lin. Exploring the structure of supervised data by Discriminant Isometric Mapping. Pattern Recognition, 38(4), Pages 599-601, 2005.
[8] Lee, J. S, 1981, Refined filtering of image noise using local statistics, Computer Graphics and Image Processing, vol. 15, pp. 380-389.
[9] He, D. C. and W. Li, 1991, Texture features based on texture spectrum, Pattern Recognition, vol. 24, no. 5, pp. 391-399.
[10] Ulaby, F. T., F. Kouyate, B. Brisco, and T. H. L. Williams, 1986, Textural information in SAR images, IEEE Trans. Geosci. Remote Sensing, vol. GE-24, no. 2, pp. 235-245