Structural SIMilarity and Spatial Frequency Motivated Atlas Database Reduction for Multi-atlas Segmentation

  • Yaqian Zhao
  • Aimin Hao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


Multi-atlas segmentation has been proved to be an effective approach in biomedical images segmentation. In this approach, all atlases in atlas database participate in segmentation process. This results in the fact that the computational cost and the redundancy bias will increase correspondingly with the size of atlas database. To decrease the computational cost, many researches proposed atlas selection which selects limited atlases to register with the query image. This strategy effectively reduces the number of atlases in registration, but is ineffective in reducing redundancy bias. To address this problem, we propose a novel strategy that improving segmentation quality through analyse of atlas database. Our contributions are summarized as follow: (1) define optimal minimum reduced atlas database (MinRAD); (2) give an algorithm of constructing optimal MinRAD based structure similarity and spatial frequency; and (3) demonstrate validity of our strategy by experimental results.


Multi-atlas segmentation Atlas database reduction Structural SIMilarity (SSIM) Spatial Frequency (SF) 


  1. 1.
    Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Thiran JP (2003) Atlas-based segmentation of pathological brain MR images. ICIP 573–576:2003Google Scholar
  2. 2.
    Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Auto-matic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–126CrossRefGoogle Scholar
  3. 3.
    Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3):726–738CrossRefGoogle Scholar
  4. 4.
    Lötjönen JM, Wolz R, Koikkalainen JR, Thurfjell L, Waldemar G, Soininen H, Rueckert D (2010) Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49(3):2352–2365CrossRefGoogle Scholar
  5. 5.
    Akinyemi A, Plakas C, Piper J, Roberts C, Poole I (2012) Optimal atlas selection using image similarities in a trained regression model to predict performance. In: Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on IEEE, pp 1264–1267Google Scholar
  6. 6.
    Tong T, Wolz R, Coupé P, Hajnal JV, Rueckert D (2013) Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. Neuroimage 76(1):11–23CrossRefGoogle Scholar
  7. 7.
    Dice L (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302CrossRefGoogle Scholar
  8. 8.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  9. 9.
    Łoza A, Mihaylova L, Bull D, Canagarajah N (2009) Structural similarity-based object tracking in multimodality surveillance videos. Mach Vis Appl 20(2):71–83CrossRefGoogle Scholar
  10. 10.
    Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 143(12):2959–2965CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Virtual Reality Technology and SystemsBeihang UniversityBeijingChina

Personalised recommendations