Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image

Edge detection is an important step in medical image processing. It aims to mark the point whereby the light intensity changed significantly. The traditional edge detectors such as Prewitt, Robert and Sobel are sensitive towards noise and sometimes inaccurate. Therefore, fuzzy approach is introduced...

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Main Authors: Suzelawati Zenian, Tahir Ahmad, Amidora Idris
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/24164/1/Fuzzy%20Inference%20System%20for%20Edge%20Detection%20in%20Flat%20Electroencephalography%20Image.pdf
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author Suzelawati Zenian
Tahir Ahmad
Amidora Idris
author_facet Suzelawati Zenian
Tahir Ahmad
Amidora Idris
author_sort Suzelawati Zenian
collection UMS
description Edge detection is an important step in medical image processing. It aims to mark the point whereby the light intensity changed significantly. The traditional edge detectors such as Prewitt, Robert and Sobel are sensitive towards noise and sometimes inaccurate. Therefore, fuzzy approach is introduced in edge detection in order to overcome the drawbacks. In this paper, fuzzy inference system (FIS) is applied to determine the boundary of the epileptic foci of Flat Electroencephalography (fEEG). The method interprets the values in the input image and according to user defined rules. The input of FIS is obtained from the fEEG input image with three types of filtering such as Sobel operator, high-pass filter and a low pass filter. Furthermore, the performance of the technique is compared with the traditional edge detectors and other fuzzy edge detectors.
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spelling ums.eprints-241642019-11-21T23:22:20Z https://eprints.ums.edu.my/id/eprint/24164/ Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image Suzelawati Zenian Tahir Ahmad Amidora Idris TA Engineering (General). Civil engineering (General) Edge detection is an important step in medical image processing. It aims to mark the point whereby the light intensity changed significantly. The traditional edge detectors such as Prewitt, Robert and Sobel are sensitive towards noise and sometimes inaccurate. Therefore, fuzzy approach is introduced in edge detection in order to overcome the drawbacks. In this paper, fuzzy inference system (FIS) is applied to determine the boundary of the epileptic foci of Flat Electroencephalography (fEEG). The method interprets the values in the input image and according to user defined rules. The input of FIS is obtained from the fEEG input image with three types of filtering such as Sobel operator, high-pass filter and a low pass filter. Furthermore, the performance of the technique is compared with the traditional edge detectors and other fuzzy edge detectors. 2018 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/24164/1/Fuzzy%20Inference%20System%20for%20Edge%20Detection%20in%20Flat%20Electroencephalography%20Image.pdf Suzelawati Zenian and Tahir Ahmad and Amidora Idris (2018) Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image. ASM Sc. J, 11 (3). pp. 147-152.
spellingShingle TA Engineering (General). Civil engineering (General)
Suzelawati Zenian
Tahir Ahmad
Amidora Idris
Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image
title Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image
title_full Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image
title_fullStr Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image
title_full_unstemmed Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image
title_short Fuzzy Inference System for Edge Detection in Flat Electroencephalography Image
title_sort fuzzy inference system for edge detection in flat electroencephalography image
topic TA Engineering (General). Civil engineering (General)
url https://eprints.ums.edu.my/id/eprint/24164/1/Fuzzy%20Inference%20System%20for%20Edge%20Detection%20in%20Flat%20Electroencephalography%20Image.pdf
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AT tahirahmad fuzzyinferencesystemforedgedetectioninflatelectroencephalographyimage
AT amidoraidris fuzzyinferencesystemforedgedetectioninflatelectroencephalographyimage