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 (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.
Department of Computer Engineering, Faculty of Engineering, Firat University, 23119 Elazig, Turkey.
Burhan
B
Ergen
D000465:Algorithms; D017143:Fuzzy Logic; D007089:Image Enhancement / Q000295:instrumentation / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed