A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection
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...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/964870 |
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| _version_ | 1832564803801972736 |
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| author | Burhan Ergen |
| author_facet | Burhan Ergen |
| author_sort | Burhan Ergen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-14aaef46f9164c8eb359a93445bfaa99 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-14aaef46f9164c8eb359a93445bfaa992025-02-03T01:10:06ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/964870964870A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge DetectionBurhan Ergen0Department of Computer Engineering, Faculty of Engineering, Firat University, 23119 Elazig, TurkeyThis 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.http://dx.doi.org/10.1155/2014/964870 |
| spellingShingle | Burhan Ergen A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection The Scientific World Journal |
| title | A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection |
| title_full | A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection |
| title_fullStr | A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection |
| title_full_unstemmed | A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection |
| title_short | A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection |
| title_sort | fusion method of gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection |
| url | http://dx.doi.org/10.1155/2014/964870 |
| work_keys_str_mv | AT burhanergen afusionmethodofgaborwavelettransformandunsupervisedclusteringalgorithmsfortissueedgedetection AT burhanergen fusionmethodofgaborwavelettransformandunsupervisedclusteringalgorithmsfortissueedgedetection |