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An image segmentation technique using nonsubsampled contourlet transform and active contours

  • Lingling FangEmail author
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Abstract

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.

Keywords

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

Abbreviations

WT

Wavelet transform

CT

Contourlet transform

NSCT

Nonsubsampled contourlet transform

DFB

Directional filter banks

HMT

Hidden Markov tree

CV

Chan–Vese

GMM

Gaussian mixture model

PDF

Probability distribution function

EM

Expectation maximization

FPR

False-positive ratio

FNR

False-negative ratio

ER

Error ratio

Notes

Acknowledgements

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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