Record number :
963048
Title of article :
Unsupervised Texture Image Segmentation Using MRF-EM Framework
Author/Authors :
آذريان، مرضيه نويسنده Department of Computer Engineering and Information Technology, Science and Research Branch, Islamic Azad University, Khouzestan-Iran Azarian, Marzieh , جواديان ، رضا نويسنده Javadian, R , عباسي دزفولي، ماشا الله نويسنده Department of Computer Engineering and Information Technology, Science and Research Branch, Islamic Azad University, Khouzestan-Iran Abbasi Dezfuli, Mashallah
Issue Information :
فصلنامه با شماره پیاپی 12 سال 2013
Pages :
13
From page :
1
To page :
13
Abstract :
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientation) values. The output image of this step clarified different textures and then used low pass Gaussian filter for smoothing the image. These two filters were used as preprocessing stage of texture images. In this research, we used K-means algorithm for initial segmentation. In this study, we used Expectation Maximization (EM) algorithm to estimate parameters, too. Finally, the segmentation was done by Iterated Conditional Modes (ICM) algorithm updating the labels and minimizing the energy function. In order to test the segmentation performance, some of the standard images of Brodatz database are used. The experimental results show the effectiveness of the proposed method.
Journal title :
Journal of Advances in Computer Research
Journal title :
Journal of Advances in Computer Research
Serial Year :
2013
Link To Document :
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