A sobolev norm based distance measure for HARDI clustering: A feasibility study on phantom and real data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6361 LNCS, Issue PART 1, 2010, Pages 175-182

  Brunenberg, Ellen ^a   Duits, Remco ^a   Ter Haar Romeny, Bart ^a   Platel, Bram ^b  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b MAASTRICHT UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN DATA; DIFFUSION PROFILES; DISSIMILARITY MEASURES; DISTANCE MEASURE; FEASIBILITY STUDIES; SOBOLEV NORMS;

MEDICAL IMAGING;

MEDICAL COMPUTING;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; CYTOLOGY; DIFFUSION WEIGHTED IMAGING; FEASIBILITY STUDY; HUMAN; IMAGE ENHANCEMENT; IMAGE QUALITY; INSTRUMENTATION; METHODOLOGY; NERVE CELL; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN; DIFFUSION MAGNETIC RESONANCE IMAGING; FEASIBILITY STUDIES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEURONS; PATTERN RECOGNITION, AUTOMATED; PHANTOMS, IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84883834865     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15705-9_22     Document Type: Conference Paper

References (13)

1. Wiegell, M.R., Tuch, D.S., Larsson, H.B.W., Wedeen, V.J.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. Neuroimage 19(2), 391-401 (2003)
2. Ziyan, U., Tuch, D., Westin, C.F.: Segmentation of thalamic nuclei from DTI using spectral clustering. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 807-814. Springer, Heidelberg (2006)
3. Grassi, A., Cammoun, L., Pollo, C., Hagmann, P., Meuli, R., Thiran, J.P.: Thalamic nuclei clustering on high angular resolution diffusion images. ISMRM 16, 1777 (2008)
4. Descoteaux, M.: High Angular Resolution Diffusion MRI: from Local Estimation to Segmentation and Tractography. PhD thesis, INRIA Sophia Antipolis (2008)
5. McGraw, T., Vemuri, B., Yezierski, R., Mareci, T.: Segmentation of high angular resolution diffusion MRI modeled as a field of von Mises-Fisher mixtures. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 463-475. Springer, Heidelberg (2006)
6. Goh, A., Lenglet, C., Thompson, P.M., Vidal, R.: A nonparametric Riemannian framework for processing high angular diffusion images (HARDI). In: CVPR, pp. 2496-2503 (2009)
7. Lindeberg, T.: Scale-space behaviour of local extrema and blobs. J. Math. Imaging Vision 1(1), 65-99 (1992)
8. Duits, R., Felsberg, M., Florack, L.: α scale spaces on a bounded domain. In: Scale Space Conference, pp. 494-510 (June 2003)
9. Felsberg, M., Duits, R., Florack, L.: The monogenic scale space on a bounded domain and its applications. Int. J. Comput. Vision 2-3(64), 187-201 (2005)
10. Duits, R., Franken, E.: Left-invariant diffusions on the space of positions and orientations and their application to crossing-preserving smoothing of hardi images. Int. J. Comput. Vision (2010), http://dx.doi.org/10. 1007/s11263-010-0332-z
11. Söderman, O., Jönsson, B.: Restricted diffusion in cylindrical geometry. J. Magn. Reson. Series A 117(1), 94-97 (1995)
12. Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143-156 (2001)
13. Brunenberg, E., Pelgrim, E., ter Haar Romeny, B., Platel, B.: k-means and graph cuts clustering of diffusion MRI in rat STN. ISMRM 18, 4045 (2010)