NDLI: Nonsubsampled Contourlet Transform based expectation maximization method for segmentation of images

Content Provider	IEEE Xplore Digital Library
Author	Meena Prakash, R. Shantha Selva Kumari, R.
Copyright Year	2012
Description	Author affiliation: CH P.S.R.Engineering College, Sivakasi, India (Meena Prakash, R.) \|\| Mepco Schlenk Engineering College, Sivakasi, India (Shantha Selva Kumari, R.)
Abstract	In this paper, a new algorithm for Gaussian Mixture Model based image segmentation is presented. The proposed method is an extension to traditional Gaussian Mixtures Expectation-Maximization Segmentation. The standard Gaussian Mixture Model(GMM) is the most widely used method for image segmentation where the model parameters can be estimated by the Expectation Maximization algorithm. The Gaussian Mixture Model considers each pixel as independent and does not take into account the spatial correlation between the neighbouring pixels. Hence the segmentation result obtained using standard GMM is highly sensitive to noise. In this paper, the Nonsubsampled Contourlet Transform is employed to overcome this drawback. The Nonsumsampled contourlet transform employs a trous filters for pyramidal decomposition which are isotropic and shift invariant. The a trous algorithm uses a low pass filter to obtain successive approximations of the original image. The low pass output of the nonsubsampled contourlet transform incorporates the spatial correlation between the neighboring pixels and hence gives better segmentation accuracy when subjected to Expectation Maximization. The presented algorithm is used to segment synthetic images and real images with regions of varying pixel intensities. The performance of the algorithm is compared with the standard GMM and the recently proposed method which addresses the pixel labeling problem by replacing each pixel value in an image with the average value of its neighbors including itself. The results suggested that the proposed algorithm is superior in segmentation accuracy when compared with the existing methods.
Starting Page	137
Ending Page	140
File Size	919225
Page Count	4
File Format	PDF
ISBN	9781467323192
e-ISBN	9781467323222
DOI	10.1109/MVIP.2012.6428779
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-14
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Expectation Maximization Image edge detection Transforms Artificial neural networks Nonhomogeneous media spatial correlation Classification algorithms Nonsubsampled Contourlet Transform Biomedical imaging pixel labeling
Content Type	Text
Resource Type	Article

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