Artificial neural network based fast edge detection algorithm for MRI medical images

Gunawan, Teddy Surya and Yaacob, Iza Zayana and Kartiwi, Mira and Ismail, Nanang and Za'bah, Nor Farahidah and Mansor, Hasmah (2017) Artificial neural network based fast edge detection algorithm for MRI medical images. Indonesian Journal of Electrical Engineering and Computer Science, 7 (1). pp. 123-130. ISSN 2502-4752 E-ISSN 2502-4760

	PDF - Published Version Restricted to Registered users only Download (929kB) | Request a copy
Preview	PDF (SCOPUS) - Supplemental Material Download (521kB) | Preview

Abstract

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector.

Item Type:	Article (Journal)
Additional Information:	5588/58372
Uncontrolled Keywords:	MRI images; artificial neural network; edge detection; Canny edge detector
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	18 Sep 2017 16:01
Last Modified:	03 Apr 2018 15:28
URI:	http://irep.iium.edu.my/id/eprint/58372

Actions (login required)

View Item