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


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.


Dynamic texture Surfacelet transform HMT model Parallel computing Ultrasound images 



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


  1. 1.
    Ackermann D, Schmitz G (2016) Detection and tracking of multiple microbubbles in ultrasound B-mode images. IEEE Trans Ultrason Ferroelectr Freq Control 63(1):72–82CrossRefGoogle Scholar
  2. 2.
    Akbari H, Fei B (2012) 3D ultrasound image segmentation using wavelet support vector machines. Med Phys 39(6):2972–2984CrossRefGoogle Scholar
  3. 3.
    Ban Z, Liu J, Cao L (2018) Superpixel segmentation using Gaussian mixture model. IEEE Trans Image Process 27(8):4105–4117MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Cary TW, Reamer CB, Sultan LR, Mohler ER, Sehqal CM (2014) Brachial artery vasomotion and transducer pressure effect on measurements by active contour segmentation on ultrasound. Med Phys 41(2):1–12CrossRefGoogle Scholar
  5. 5.
    Ding J, Cheng HD, Huang J, Liu J, Zhang Y (2012) Breast ultrasound image classification based on multiple-instance learning. J Digit Imaging 25(5):620–627CrossRefGoogle Scholar
  6. 6.
    Faisal A, Ng SC, Goh SL, George J, Supriyanto E, Lai KW (2015) Multiple LREK active contours for knee meniscus ultrasound image segmentation. IEEE Trans Med Imaging 34(10):2162–2171CrossRefGoogle Scholar
  7. 7.
    Gómez W, Pereira WCA, Infantosi AFC (2012) Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans Med Imaging 31(10):1889–1899CrossRefGoogle Scholar
  8. 8.
    Hajati F, Tavakolian M, Gheisari S, Gao Y, Mian AS (2017) Dynamic texture comparison using derivative sparse representation: application to video-based face recognition. IEEE Trans Human-Mach Syst 47(6):970–982CrossRefGoogle Scholar
  9. 9.
    Hassan M, Chaudhry A, Khan A, Iftikhar MA (2014) Robust information gain based fuzzy c-means clustering and classification of carotid artery ultrasound images. Comput Methods Prog Biomed 113(2):593–609CrossRefGoogle Scholar
  10. 10.
    Krishnan KR, Radhakrishnan S (2017) Hybrid approach to classification of focal and diffused liver disorders using ultrasound images with wavelets and texture features. IET Image Process 11(7):530–538CrossRefGoogle Scholar
  11. 11.
    Liao C, Tao J, Zeng X, Su Y, Zhou D, Li X (2016) Efficient spatial variation modeling of nanoscale integrated circuits via hidden Markov tree. IEEE Trans Comput-Aided Des Integ Circ Syst 35(6):971–984CrossRefGoogle Scholar
  12. 12.
    Liu Y, Cheng HD, Huang J, Zhang Y, Tang X (2012) An effective approach of lesion segmentation within the breast ultrasound image based on the cellular automata principle. J Digit Imaging 25(5):580–590CrossRefGoogle Scholar
  13. 13.
    Loizou CP, Pattichis CS, Pantziaris M, Kyriacou E, Nicolaides A (2017) Texture feature variability in ultrasound video of the atherosclerotic carotid plaque. IEEE J Translat Eng Health Med 5(99):1800509Google Scholar
  14. 14.
    Lu Y, Do MN (2007) Multidimensional directional filterbanks and surfacelets. IEEE Trans Image Process 16(4):918–931MathSciNetCrossRefGoogle Scholar
  15. 15.
    Machucho-Cadena R, Rivera-Rovelo J, Bayro-Corrochano E (2014) Geometric techniques for 3D tracking of ultrasound sensor, tumor segmentation in ultrasound images, and 3D reconstruction. Pattern Recogn 47(5):1968–1987CrossRefGoogle Scholar
  16. 16.
    Mahdavi SS, Moradi M, Morris WJ, Goldenberg SL, Salcudean SE (2012) Fusion of ultrasound B-mode and vibro-elastography images for automatic 3-D segmentation of the prostate. IEEE Trans Med Imaging 31(11):2073–2082CrossRefGoogle Scholar
  17. 17.
    Mendizabal-Ruiz EG, Rivera M, Kakadiaris IA (2013) Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach. Med Image Anal 17:649–670CrossRefGoogle Scholar
  18. 18.
    Nguyen NQ, Prager RW (2016) High-resolution ultrasound imaging with unified pixel-based beamforming. IEEE Trans Med Imaging 35(1):98–108CrossRefGoogle Scholar
  19. 19.
    Pazinato DV, Stein BV, de Almeida WR, de Werneck RO, Júnior PRM, Penatti OAB, da Torres RS, Menezes FH, Rocha A (2016) Pixel-level tissue classification for ultrasound images. IEEE J Biomed Health Inform 20(1):256–267CrossRefGoogle Scholar
  20. 20.
    Pereyra M, Dobigeon N, Batatia H, Tourneret JY (2012) Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model. IEEE Trans Med Imaging 31(8):1509–1520CrossRefGoogle Scholar
  21. 21.
    Ravichandran A, Chaudhry R, Vidal R (2013) Categorizing dynamic textures using a bag of dynamical systems. IEEE Trans Pattern Anal Mach Intell 35(2):342–353CrossRefGoogle Scholar
  22. 22.
    Rezaeifar B, Saadatmand-Tarzjan M (2017) A new algorithm for multimodal medical image fusion based on the surfacelet transform. 7th Int Conf Comput Knowledge Eng (ICCKE): 396–400Google Scholar
  23. 23.
    Rueda S, Fathima S, Knight CL, Yaqub M, Papageorghiou AT, Rahmatullah B, Foi A, Maggioni M, Pepe A, Tohka J, Stebbing RV, McManigle JE, Ciurte A, Bresson X, Cuadra MB, Sun C, Ponomarev GV, Gelfand MS, Kazanov MD, Wang CW, Chen HC, Peng CW, Hung CM, Noble JA (2014) Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imaging 33(4):797–813CrossRefGoogle Scholar
  24. 24.
    Shin J, Huang L (2017) Spatial prediction filtering of acoustic clutter and random noise in medical ultrasound imaging. IEEE Trans Med Imaging 36(2):396–406CrossRefGoogle Scholar
  25. 25.
    Sridar P, Kumar A, Li C, Woo J, Quinton A, Benzie R, Peek MJ, Feng D, Kumar RK, Nanan R, Kim J (2017) Automatic measurement of thalamic diameter in 2-D fetal ultrasound brain images using shape prior constrained regularized level sets. IEEE J Biomed Health Inform 21(4):1069–1078CrossRefGoogle Scholar
  26. 26.
    Tan T, Platel B, Mus R, Tabar L, Mann RM, Karssemeijer N (2013) Computer-aided detection of cancer in automated 3-D breast ultrasound. IEEE Trans Med Imaging 32(9):1698–1706CrossRefGoogle Scholar
  27. 27.
    Tian XL, Jiao LC, Duan Y, Zhang XH (2014) Video denoising via spatially adaptive coefficient shrinkage and threshold adjustment in surfacelet transform domain. SIViP 8:901–912CrossRefGoogle Scholar
  28. 28.
    Torbati N, Ayatollahi A, Kermani A (2014) An efficient neural network based method for medical image segmentation. Comput Biol Med 44:76–87CrossRefGoogle Scholar
  29. 29.
    Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS (2011) Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed 15(1):130–137CrossRefGoogle Scholar
  30. 30.
    Xu X, Zhou Y, Cheng X, Song E, Li G (2012) Ultrasound intima–media segmentation using Hough transform and dual snake model. Comput Med Imaging Graph 36(3):248–258CrossRefGoogle Scholar
  31. 31.
    Yang X, Jin J, Xu M, Wu H, He W, Chi MY, Ding M (2013) Ultrasound common carotid artery segmentation based on active shape model. Comput Math Methods Med 2013:1–11Google Scholar
  32. 32.
    Zhao X, Lin Y, Heikkilä J (2018) Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection. IEEE Trans Multimed 20(3):552–566CrossRefGoogle Scholar
  33. 33.
    Zhou Y, Cheng X, Xu X, Song E (2013) Dynamic programming in parallel boundary detection with application to ultrasound intima-media segmentation. Med Image Anal 17(8):892–906CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina

Personalised recommendations