Soft Computing

, Volume 23, Issue 6, pp 1823–1832 | Cite as

An image segmentation technique using nonsubsampled contourlet transform and active contours

  • Lingling FangEmail author


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.


Image segmentation The multiresolution representation Nonsubsampled contourlet transform (NSCT) Active contours 



Wavelet transform


Contourlet transform


Nonsubsampled contourlet transform


Directional filter banks


Hidden Markov tree




Gaussian mixture model


Probability distribution function


Expectation maximization


False-positive ratio


False-negative ratio


Error ratio



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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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer and Information TechnologyLiaoning Normal UniversityDalian CityChina

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