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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5381–5401 | Cite as

A dynamic texture based segmentation method for ultrasound images with Surfacelet, HMT and parallel computing

  • Bo Cai
  • Wei Ye
  • Jianhui ZhaoEmail author
Article
  • 73 Downloads

Abstract

To segment regions of interest (ROIs) from ultrasound images, one novel dynamic texture based algorithm is presented with surfacelet transform, hidden Markov tree (HMT) model and parallel computing. During surfacelet transform, the image sequence is decomposed by pyramid model, and the 3D signals with high frequency are decomposed by directional filter banks. During HMT modeling, distribution of coefficients is described with Gaussian mixture model (GMM), and relationship of scales is described with scale continuity model. From HMT parameters estimated through expectation maximization, the joint probability density is calculated and taken as feature value of image sequence. Then ROIs and non-ROIs in collected sample videos are used to train the support vector machine (SVM) classifier, which is employed to identify the divided 3D blocks from input video. To improve the computational efficiency, parallel computing is implemented with multi-processor CPU. Our algorithm has been compared with the existing texture based approaches, including gray level co-occurrence matrix (GLCM), local binary pattern (LBP), Wavelet, for ultrasound images, and the experimental results prove its advantages of processing noisy ultrasound images and segmenting higher accurate ROIs.

Keywords

Dynamic texture Surfacelet transform HMT model Parallel computing Ultrasound images 

Notes

Funding

This work was supported by National Basic Research Program of China (973 Program, No. 2011CB707904).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

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