Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set

v ABSTRACT This study focused on contrast enhancement of Flat Electroencephalography (fEEG) image during epileptic seizure. The main interest is in visualizing the path of brainstorm in the brain that occur during seizure. Selected techniques that are involved ranging from classical, ordinary fuzzy,...

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Main Author:	Zenian, Suzelawati
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/79542/1/SuzelawatiZenianPFS2018.pdf
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spelling	my-utm-ep.795422018-10-31T12:58:12Z Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set 2018 Zenian, Suzelawati QA Mathematics v ABSTRACT This study focused on contrast enhancement of Flat Electroencephalography (fEEG) image during epileptic seizure. The main interest is in visualizing the path of brainstorm in the brain that occur during seizure. Selected techniques that are involved ranging from classical, ordinary fuzzy, and advanced fuzzy namely the intuitionistic fuzzy sets (IFS). Different techniques may result in different output of fEEG image. The methods in classical approach are Power Law Transformation, Histogram Equalization, and Image Size Dependent Normalization. The intensifier operator is implemented in the fuzzy contrast enhancement technique. For the IFS approach, the Window Based Enhancement Scheme (WBES) and its revised version (RWBES) are applied. The RWBES gives better results compared to the WBES whereby the vague boundary of the cluster centres are reduced resulting in a smaller area of the vague boundary. The vague boundary represents the strength of the electrical potential of the foci of seizure. Next, the quality of the output image is measured via the objective measure such as mean squared error (MSE), peak-signalto- noise-ratio (PSNR), universal image quality index (UIQI), and structural similarity index measure (SSIM). In IFS, the sum of membership and non-membership is not necessarily equal to one. Thus, there exists hesitancy in deciding the degree to which an element satisfies a particular property. Moreover, the sequence of enhanced fEEG images are demonstrated by varying the value of parameter, namely λ, that also influence the hesitation value π. In addition, the Sugeno type intuitionistic fuzzy generator which is used to compute the non-membership value v has been extended to the concept of fuzzy limit. Hence, by implementing the definition of fuzzy limit, different values of ∈ will be tested in obtaining the values of integer N that will determine the value of λ and hence the value of hesitation π. The relationship between membership, non-membership, and hesitation values are also demonstrated graphically. 2018 Thesis http://eprints.utm.my/id/eprint/79542/ http://eprints.utm.my/id/eprint/79542/1/SuzelawatiZenianPFS2018.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Zenian, Suzelawati Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
description	v ABSTRACT This study focused on contrast enhancement of Flat Electroencephalography (fEEG) image during epileptic seizure. The main interest is in visualizing the path of brainstorm in the brain that occur during seizure. Selected techniques that are involved ranging from classical, ordinary fuzzy, and advanced fuzzy namely the intuitionistic fuzzy sets (IFS). Different techniques may result in different output of fEEG image. The methods in classical approach are Power Law Transformation, Histogram Equalization, and Image Size Dependent Normalization. The intensifier operator is implemented in the fuzzy contrast enhancement technique. For the IFS approach, the Window Based Enhancement Scheme (WBES) and its revised version (RWBES) are applied. The RWBES gives better results compared to the WBES whereby the vague boundary of the cluster centres are reduced resulting in a smaller area of the vague boundary. The vague boundary represents the strength of the electrical potential of the foci of seizure. Next, the quality of the output image is measured via the objective measure such as mean squared error (MSE), peak-signalto- noise-ratio (PSNR), universal image quality index (UIQI), and structural similarity index measure (SSIM). In IFS, the sum of membership and non-membership is not necessarily equal to one. Thus, there exists hesitancy in deciding the degree to which an element satisfies a particular property. Moreover, the sequence of enhanced fEEG images are demonstrated by varying the value of parameter, namely λ, that also influence the hesitation value π. In addition, the Sugeno type intuitionistic fuzzy generator which is used to compute the non-membership value v has been extended to the concept of fuzzy limit. Hence, by implementing the definition of fuzzy limit, different values of ∈ will be tested in obtaining the values of integer N that will determine the value of λ and hence the value of hesitation π. The relationship between membership, non-membership, and hesitation values are also demonstrated graphically.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Zenian, Suzelawati
author_facet	Zenian, Suzelawati
author_sort	Zenian, Suzelawati
title	Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
title_short	Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
title_full	Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
title_fullStr	Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
title_full_unstemmed	Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
title_sort	sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2018
url	http://eprints.utm.my/id/eprint/79542/1/SuzelawatiZenianPFS2018.pdf
_version_	1747818251817058304

Sequence of image enhancement of flat electroencephalography using intuitionistic fuzzy set

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