A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-23
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms.
The GWT is used to enhance the edge information in an image while suppressing noise.
Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image.
The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image.
The results prove that the proposed methods are successful for edge detection, even in noisy cases.
American Psychological Association (APA)
Ergen, Burhan. 2014. A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1051782
Modern Language Association (MLA)
Ergen, Burhan. A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection. The Scientific World Journal No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-1051782
American Medical Association (AMA)
Ergen, Burhan. A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1051782
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051782