UJA-3DFD: A program to compute the 3D fractal dimension from MRI data

Computer Methods and Programs in Biomedicine

Volumn 104, Issue 3, 2011, Pages 452-460

  Ruiz de Miras, J ^a   Navas, J ^a   Villoslada, P ^b   Esteban, F J ^a  

^a UNIVERSITY OF JAÉN   (Spain)

^b INSTITUT D INVESTIGACIONS BIOMÈDIQUES AUGUST PI I SUNYER IDIBAPS   (Spain)

Author keywords

3D box counting; Fractal dimension; Magnetic resonance imaging

Indexed keywords

BOX-COUNTING; COMPUTER PROGRAM; SKELETONIZATION;

FRACTAL DIMENSION; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES; PARTIAL DISCHARGES;

THREE DIMENSIONAL;

ACCURACY; ALGORITHM; ARTICLE; COMPUTER PROGRAM; DEGENERATIVE DISEASE; NUCLEAR MAGNETIC RESONANCE IMAGING; UJA THREE DIMENSIONAL FRACTAL DIMENSION; VALIDITY;

ALGORITHMS; FRACTALS; HUMANS; MAGNETIC RESONANCE IMAGING; SOFTWARE;

EID: 80655143530     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2010.08.015     Document Type: Article

References (23)

1. Zhang L., Butler A.J., Sun C.K., Sahgal V., Wittenberg G.F., Yue G.H. Fractal dimension assessment of brain white matter structural complexity post stroke in relation to upper-extremity motor function. Brain Research 2008, 1228:229-240.
2. Free S.L., Sisodiya S.M., Cook M.J., Fish D.R., Shorvon S.D. Three-dimensional fractal analysis of the white matter surface from magnetic resonance images of human brain. Cerebral Cortex 1996, 6:830-836.
3. Lopes R., Betrouni N. Fractal and multifractal analysis: a review. Medical Image Analysis 2009, 13:634-649.
4. Jiang J., Zhu W., Shi F., Zhang Y., Lin L., Jiang T. A robust and accurate algorithm for estimating the complexity of the cortical surface. Journal of Neurosciences Methods 2008, 172:122-130.
5. Yoshikawa T., Murase K., Oku N., Imaizumi M., Takasawa M., Rishu P., Kimura Y., Ikejiri Y., Kitagawa K., Hori M., Hatazawa J. Heterogeneity of cerebral blood flow in Alzheimer disease and vascular dementia. American Journal of Neuroradiology 2003, 24:1341-1347.
6. Nagao M., Sugawara Y., Ikeda M., Fukuhara R., Ishikawa T., Murase K., Kikuchi T., Mochizuki T., Miki H. Heterogeneity of posterior limbic perfusion in very early Alzheimer's disease. Neuroscience Research 2006, 55:285-291.
7. Liu J.Z., Zhang L.D., Yue G.H. Fractal dimension in human cerebellum measured by magnetic resonance imaging. Biophysical Journal 2003, 85:4041-4046.
8. Zhang L., Liu J.Z., Dean D., Sahgal V., Yue G.H. A three-dimensional fractal analysis method for quantifying white matter structure in human brain. Journal of Neuroscience Methods 2006, 150:242-253.
9. Zhang L., Dean D., Liu J.Z., Sahgal V., Wang X., Yue G.H. Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiology of Aging 2007, 28:1543-1555.
10. Saghi Z., Xu X., Möbus G. Three-dimensional metrology and fractal analysis of dendritic nanostructures. Physical Review 2008, 78. 205428-1-5.
11. Wu Y.T., Shyu K.K., Chen T.R., Guo W.Y. Using three-dimensional fractal dimension to analyze the complexity of fetal cortical surface from magnetic resonance images. Nonlinear Dynamics 2009, 58:745-752.
12. Shyu K.K., Wu Y.T., Chen T.R., Chen H.Y., Hu H.H., Guo W.Y. Analysis of fetal cortical complexity from MR images using 3D entropy based information fractal dimension. Nonlinear Dynamics 2010, 61:363-372.
13. Wu Y.T., Shyu K.K., Jao C.W., Wang Z.Y., Soong B.W., Wu H.M., Wang P.S. Fractal dimension analysis for quantifying cerebellar morphological change of multiple system atrophy of the cerebellar type (MSA-C). NeuroImage 2010, 49:539-551.
14. Grossu I.V., Besliua C., Rusua M.V., Jipaa A.L., Bordeianua C.C., Felea D. Visual tool for estimating the fractal dimension of images. Computer Physics Communications 2009, 180:1999-2001.
15. Wang Q., Wang Q., Mei X., Zhang H., Sun H. Quick computation program of fractal dimension for 2D vector data. Proceedings of the 17th International Conference on GeoInformatics 2009, 1-4.
16. Sezer E. A computer program for fractal dimension (FRACEK) with application on type of mass movement characterization. Computers and Geosciences 2010, 36:391-396.
17. Volume Graphics 2000, Springer. M. Chen, A.E. Kaufman, R. Yagel (Eds.).
18. Svensson S., Nystrom I., Sanniti di Baja G. Curve-skeletonization of surface-like objects in 3D images guided by voxel classification. Pattern Recognition Letters 2002, 23:1419-1426.
19. Cornea N.D., Silver D., Min P. Curve-skeleton properties, applications and algorithms. IEEE Transactions on Visualization and Computer Graphics 2007, 13:530-548.
20. Palagyi K, Kuba A. A parallel 3D 12-subiteration thinning algorithm. Graphical Models and Image Processing 1999, 61:199-221.
21. Esteban F.J., Sepulcre J., Vélez de Mendizábal N., Goñi J., Navas J., Ruiz de Miras J., Bejarano B., Masdeu J.C., Villoslada P. Fractal dimension and white-matter changes in multiple sclerosis. NeuroImage 2007, 36:543-549.
22. Esteban F.J., Sepulcre J., Ruiz de Miras J., Navas J., de Mendizábal N.V., Goñi J., Quesada J.M., Bejarano B., Villoslada P. Fractal dimension analysis of grey matter in multiple sclerosis. Journal of Neurological Sciences 2009, 282:67-71.
23. F.J. Esteban, N. Padilla, M. Sanz-Cortés, J. Ruiz de Miras, N. Bargalló, P. Villoslada, E. Gratacós, Fractal-dimension analysis detects cerebral changes in preterm infants with and without intrauterine growth restriction, NeuroImage, in press, doi:10.1016/j.neuroimage.2010.07.019.