Edge detection of MRI images using artificial neural network

Introduction Many methods have been proposed for MRI tissue segmentation. It has been identified that MRI image of human tissue is homogeneous and the structure of each is tissue connected, but it is rather difficult to separate the adjacent tissue due to the small intensity changes or smooth bound...

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Main Authors:	Gunawan, Teddy Surya, Kartiwi, Mira, Abdul Malik, Noreha
Format:	Article
Language:	English English
Published:	Malaysian Medical Association 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/59506/ http://irep.iium.edu.my/59506/ http://irep.iium.edu.my/59506/2/MJM_v72-Supp-1-2017.pdf http://irep.iium.edu.my/59506/8/59506_Edge%20Detection%20of%20MRI%20Images%20using_abstract_complete.pdf

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spelling	iium-595062017-11-21T07:15:51Z http://irep.iium.edu.my/59506/ Edge detection of MRI images using artificial neural network Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha TK Electrical engineering. Electronics Nuclear engineering Introduction Many methods have been proposed for MRI tissue segmentation. It has been identified that MRI image of human tissue is homogeneous and the structure of each is tissue connected, but it is rather difficult to separate the adjacent tissue due to the small intensity changes or smooth boundaries observed. Current traditional edge detection performance could be further improved using artificial neural network (ANN) based edge segmentation. Methods In this study, various existing edge detectors techniques based on spatial domain were evaluated. The best algorithm was selected subjectively, i.e. Canny edge detector, and is used as the training data for the proposed ANN. For each pixel of grayscale image, we obtain three features, i.e. horizontal (dx), vertical (dy), and diagonal (dz). The feedforward neural network was configured to have 1 input, 1 hidden, and 1 output. To determine the final pixel edge value (0 or 1), an optimum threshold was utilized. Results To obtain the best parameters which produce optimum edge of MRI images, we varied the number of neurons in the hidden layer, as well as the threshold. We found that the optimum parameter could be achieved by setting the number of neurons in the hidden layer to be 180, and the threshold to be 0.1 for various MRI images tested. We also found that the proposed ANN based edge detection has faster computation by almost three times compared to traditional Canny edge detector. Discussion The proposed ANN based edge detection produce better image segmentation for MRI images compared to other traditional edge detection algorithms. Moreover, the computational cost is smaller by almost three times compared to Canny edge detector. It is believe that such findings would allow medical practitioners to better obtain information that could be extracted from MRI images. Malaysian Medical Association 2017-08-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/59506/2/MJM_v72-Supp-1-2017.pdf application/pdf en http://irep.iium.edu.my/59506/8/59506_Edge%20Detection%20of%20MRI%20Images%20using_abstract_complete.pdf Gunawan, Teddy Surya and Kartiwi, Mira and Abdul Malik, Noreha (2017) Edge detection of MRI images using artificial neural network. The Medical Journal of Malaysia, 72 (Supplement 1). p. 22. ISSN 0300-5283 http://www.e-mjm.org/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha Edge detection of MRI images using artificial neural network
description	Introduction Many methods have been proposed for MRI tissue segmentation. It has been identified that MRI image of human tissue is homogeneous and the structure of each is tissue connected, but it is rather difficult to separate the adjacent tissue due to the small intensity changes or smooth boundaries observed. Current traditional edge detection performance could be further improved using artificial neural network (ANN) based edge segmentation. Methods In this study, various existing edge detectors techniques based on spatial domain were evaluated. The best algorithm was selected subjectively, i.e. Canny edge detector, and is used as the training data for the proposed ANN. For each pixel of grayscale image, we obtain three features, i.e. horizontal (dx), vertical (dy), and diagonal (dz). The feedforward neural network was configured to have 1 input, 1 hidden, and 1 output. To determine the final pixel edge value (0 or 1), an optimum threshold was utilized. Results To obtain the best parameters which produce optimum edge of MRI images, we varied the number of neurons in the hidden layer, as well as the threshold. We found that the optimum parameter could be achieved by setting the number of neurons in the hidden layer to be 180, and the threshold to be 0.1 for various MRI images tested. We also found that the proposed ANN based edge detection has faster computation by almost three times compared to traditional Canny edge detector. Discussion The proposed ANN based edge detection produce better image segmentation for MRI images compared to other traditional edge detection algorithms. Moreover, the computational cost is smaller by almost three times compared to Canny edge detector. It is believe that such findings would allow medical practitioners to better obtain information that could be extracted from MRI images.
format	Article
author	Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha
author_facet	Gunawan, Teddy Surya Kartiwi, Mira Abdul Malik, Noreha
author_sort	Gunawan, Teddy Surya
title	Edge detection of MRI images using artificial neural network
title_short	Edge detection of MRI images using artificial neural network
title_full	Edge detection of MRI images using artificial neural network
title_fullStr	Edge detection of MRI images using artificial neural network
title_full_unstemmed	Edge detection of MRI images using artificial neural network
title_sort	edge detection of mri images using artificial neural network
publisher	Malaysian Medical Association
publishDate	2017
url	http://irep.iium.edu.my/59506/ http://irep.iium.edu.my/59506/ http://irep.iium.edu.my/59506/2/MJM_v72-Supp-1-2017.pdf http://irep.iium.edu.my/59506/8/59506_Edge%20Detection%20of%20MRI%20Images%20using_abstract_complete.pdf
first_indexed	2023-09-18T21:24:19Z
last_indexed	2023-09-18T21:24:19Z
_version_	1777412089283870720

Edge detection of MRI images using artificial neural network

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