M., B. (1999). TEXTURAL SEGMENTATION OF MR BRAIN IMAGES USING FUZZY LOGIC ALGORITHMS. test, 2(2nd International Conference on Electrical Engineering ICEENG 1999), 218-230. doi: 10.21608/iceeng.1999.62502
BADAWI. A. M.. "TEXTURAL SEGMENTATION OF MR BRAIN IMAGES USING FUZZY LOGIC ALGORITHMS". test, 2, 2nd International Conference on Electrical Engineering ICEENG 1999, 1999, 218-230. doi: 10.21608/iceeng.1999.62502
M., B. (1999). 'TEXTURAL SEGMENTATION OF MR BRAIN IMAGES USING FUZZY LOGIC ALGORITHMS', test, 2(2nd International Conference on Electrical Engineering ICEENG 1999), pp. 218-230. doi: 10.21608/iceeng.1999.62502
M., B. TEXTURAL SEGMENTATION OF MR BRAIN IMAGES USING FUZZY LOGIC ALGORITHMS. test, 1999; 2(2nd International Conference on Electrical Engineering ICEENG 1999): 218-230. doi: 10.21608/iceeng.1999.62502

TEXTURAL SEGMENTATION OF MR BRAIN IMAGES USING FUZZY LOGIC ALGORITHMS

The International Conference on Electrical Engineering
Article 25, Volume 2, 2nd International Conference on Electrical Engineering ICEENG 1999, November 1999, Pages 218-230    PDF (3.42 M)
Document Type: Original Article
DOI: 10.21608/iceeng.1999.62502
Author
BADAWI. A. M.
Ph.D. Department of Biomedical Engineering & Systems, Faculty of Engineering, Cairo University, Egypt
Abstract
This paper presents novel algorithms for magnetic resonance (MR) brain images segmentation using textural analysis. Classification for MR images using features extracted from the texture is done using two algorithms, the Fuzzy Rule Based system and Fuzzy Similarity measures. The cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). Image preprocessing was first done to improve the quality of brain MR images and reducing artifacts. The feature vector was selected to vary according to the textural structure of the images. The two algorithms are of supervised nature where in the first we build fuzzy rules while in the second we build fuzzy prototypes. The classification in the first method uses fuzzy inference and implication techniques to derive the classes of images. The classification in the second method uses pattern matching and fuzzy similarity measures. These algorithms are tested using sets of MR brain images. The results showed the efficient and robust performance of these algorithms. In this paper a comparison of these algorithms with Fuzzy C-Means algorithm based on texture features is presented.
Keywords
Biomedical Application; Cerebrospinal Fluid; Feature Identification and Classification; Fuzzy C-Means; Fuzzy Logic; Fuzzy Rule-Based System; Fuzzy Similarity Measures; Gray Matter; Image Segmentation; Image processing; Magnetic Resonance Imaging; Texture analysis; Supervised Learning; White Matter
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