An image segmentation technique using nonsubsampled contourlet transform and active contours
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In this paper, an unsupervised image segmentation technique is proposed. Firstly, for obtaining a multiresolution representation of the original image, the probability model of the nonsubsampled contourlet coefficients of the image is established. A region-based active contour model is then applied to the multiresolution representation for segmenting the image. The proposed technique has been conducted on challenging images to illustrate the robust and accurate segmentations. At last, an in-depth study of the behaviors of the above techniques in response to the proposed model is given, and the segmentation results are compared with several state-of-the-art methods.
KeywordsImage segmentation The multiresolution representation Nonsubsampled contourlet transform (NSCT) Active contours
Nonsubsampled contourlet transform
Directional filter banks
Hidden Markov tree
Gaussian mixture model
Probability distribution function
This work was supported by the Post-Doctoral Science Foundation of China under Grant 2017M621130, the National Natural Science Foundation of China under Grant 61801202, 61702244, 41671439, and the University Innovation Team Support Program of Liaoning Province under Grant LT2017013.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
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