Objective Evaluation of Mathematical Morphology Edge Detection on Computed Tomography (CT) Images
Authors: Emhimed Saffor, Abdelkader Salama
Abstract:
In this paper problem of edge detection in digital images is considered. Edge detection based on morphological operators was applied on two sets (brain & chest) ct images. Three methods of edge detection by applying line morphological filters with multi structures in different directions have been used. 3x3 filter for first method, 5x5 filter for second method, and 7x7 filter for third method. We had applied this algorithm on (13 images) under MATLAB program environment. In order to evaluate the performance of the above mentioned edge detection algorithms, standard deviation (SD) and peak signal to noise ratio (PSNR) were used for justification for all different ct images. The objective method and the comparison of different methods of edge detection, shows that high values of both standard deviation and PSNR values of edge detection images were obtained.
Keywords: Medical images, Matlab, Edge detection.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087802
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[1] S. K Bandyopadhyay, "Edge Detection in Brain Images", International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 2, pp. 884-887, 2011.
[2] Mehena, "Medical Images Edge Detection Based on Mathematical Morphology", International Journal of Computer & Communication Technology (IJCCT), Vol. 2, Issue VI, 2011.
[3] M. K, Sukhwinder, "Edge Detection and Demising Medical Image using Morphology", International Journal of Engineering Sciences & Emerging Technologies (IJESET), Vol. 2, Issue 2, pp. 66-72 ,Jun 2012.
[4] K.. Sreedhar, B. Panlal, "Enhancement of Images using Morphological Transformations", International Journal of Computer Science & Information Technology (IJCSIT), Vol. 4, No. 1, Feb 2012.
[5] J. A. Jiang, C. L. Chuang, and Y. L. Fahn "Mathematical Morphology Based Edge Detectors for Detection of Thin Edges in Low Contrast Regions", The Institution of Engineering and Technology (IET), Vol. 1, No. 3, pp. 269–277, Sep. 2007.
[6] R. C. Gonzalez and R. E. Woods, "Digital Image Processing", 2nd Edition, Prentice Hall, 2002.
[7] R. C. Gonzalez and R. E. Woods. Digital image Processing using MATLAB, (second edition), Galesmark Publishing, ISBN, USA.
[8] G. A, Kudale, M. D, Pawar, "Study and Analysis of Various Edge Detection Methods for X-Ray Images", International Journal of Computer Science and Application Issue, 2010.
[9] B. K, Anil Garg "Comparative Study of Different Edge Detection Techniques", International Journal of Engineering Science and Technology, Vol. 3, No. 3, pp. 1927-1935, March 2011.
[10] R. M, Himanshu Aggarwal, "Study and Comparison of Various Image Edge Detection Techniques", International Journal of Image Processing (IJIP), Vol. 3, Issue 1, 2009.