3D image texture analysis of simulated and real-world vascular trees

Computer Methods and Programs in Biomedicine

Volumn 107, Issue 2, 2012, Pages 140-154

  Kociński, Marek ^a   Klepaczko, Artur ^a   Materka, Andrzej ^a   Chekenya, Martha ^b   Lundervold, Arvid ^b  

^a LODZ UNIVERSITY OF TECHNOLOGY   (Poland)

^b UNIVERSITY OF BERGEN   (Norway)

Author keywords

3D texture analysis; Cancerous tissue classification; Simulation of vascular tree growth; Texture classification; Unsupervised texture clustering; Vascularity modeling

Indexed keywords

3D TEXTURES; CANCEROUS TISSUES; TEXTURE CLASSIFICATION; UNSUPERVISED TEXTURE CLUSTERING; VASCULAR TREES;

CARDIOVASCULAR SYSTEM; CONFOCAL MICROSCOPY; DIAGNOSIS; FORESTRY; IMAGE TEXTURE; MAGNETIC RESONANCE IMAGING; TEXTURES; THREE DIMENSIONAL COMPUTER GRAPHICS; TISSUE;

THREE DIMENSIONAL;

PROTEOGLYCAN;

ANGIOGENESIS; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; BLOOD FLOW; BLOOD VISCOSITY; BRAIN BLOOD VESSEL; BRAIN TISSUE; CONFOCAL MICROSCOPY; CONTROLLED STUDY; IMAGE ANALYSIS; NONHUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSICAL PARAMETERS; QUANTITATIVE ANALYSIS; RAT; RETINA IMAGE; SIGNAL NOISE RATIO; THREE DIMENSIONAL IMAGING; VASCULARIZATION;

ALGORITHMS; ANIMALS; BRAIN NEOPLASMS; CEREBRAL ANGIOGRAPHY; COMPUTER SIMULATION; GLIOMA; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MICROSCOPY, CONFOCAL; MODELS, BIOLOGICAL; NEOVASCULARIZATION, PATHOLOGIC; PATTERN RECOGNITION, AUTOMATED; RATS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

CLASSIFICATION; COMPUTER GRAPHICS; DIAGNOSIS; FORESTRY; IMAGE ANALYSIS; MAGNETIC RESONANCE; TEXTURE; THREE DIMENSIONAL DESIGN; TISSUE;

EID: 84861968427     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.06.004     Document Type: Article

References (42)

1. Kirbas C., Quek F. A review of vessel extraction techniques and algorithms. ACM Computing Surveys 2004, 36(2):81-121. 0360-0300.
2. Haacke M.E., Brown R.W., Thompson M.R., Venkatesan R. Magnetic Resonance Imaging: Physical Principles and Sequence Design 1999, Wiley-Liss.
3. Tofts P. Quantitative MRI of the Brain Measuring Changes Caused by Disease 2004, Wiley-VCH.
4. Bhuiyan A., Nath B., Chua J.J., Ramamohanarao K. Blood vessel segmentation from color retinal images using unsupervised texture classification. ICIP, vol. 5 2007, 521-524.
5. Texture Analysis for Magnetic Resonance Imaging 2006, Med4publishing. M. Hajek, M. Dezortova, A. Materka, R. Lerski (Eds.).
6. M. Unser, Texture classification and segmentation using wavelet frames, IEEE Transactions on Image Processing 4 (11) (1995) 1549-1560. ISSN: 1057-7149.
7. M. Marino, Receptor possibly a roadblock for tumor blood vessel formation, 2004. http://www.mc.vanderbilt.edu/reporter/index.html%3FID=3294.
8. Kretowski M., Rolland Y., Bezy-Wendling J., Coatrieux J.-L. Fast algorithm for 3-D vascular tree modeling. Computer Methods and Programs in Biomedicine 2003, 70:129-136.
9. Karch R., Neumann F., Neumann M., Szawlowski P., Schreiner W. Voronoi polyhedra analysis of oprimized arterial tree models. Annals of Biomedical Engineering 2003, 31:548-563.
10. Guyton A. Textbook of Medical Physiology 2000, W.B. Saunders, Philadelphia.
11. Chen J., Amini A.A. Quantifying 3-D vascular structures in MRA images using hybrid PDE and geometric deformable models. IEEE Transactions on Medical Imaging 2004, 23(10):1251-1262.
12. Kwan R., Evans A., Pike G. MRI simulation-based evaluation of image-processing and classification methods. IEEE Transaction on Medical Imaging 1999, 18:1085-1097.
13. Benoit-Cattin H., Collewet G., Belaroussi B., Saint-Jalmes H., Odet C. The SIMRI project: a versatile and interactive MRI simulator. Journal of Magnetic Resonance 2005, 173:97-115.
14. Benoit-Cattin H., Bellet F., Montagnat J., Odet C. Magnetic resonance imaging (MRI) simulation on a grid computing architecture. IEEE CGIGRID BIOGRID 2003.
15. J. Bezy-Wendling, M. Kretowski, Y. Rolland, W. Le Bidon, Toward a better understanding of texture in vascular CT scan simulated images, IEEE Transactions on Biomedical Engineering 48(1) (2001) 120-123. ISSN: 0018-9294.
16. Rosenfeld A., Kak A. Digital Picture Processing 1982, vol. 1. Academic Press.
17. Levine M. Vision by Man and Machine 1985, McGraw-Hill.
18. P. Brodatz, Textures - a photographic album for artists and designers, 1966. http://www.ux.uis.no/tranden/brodatz.html.
19. European Cooperation in Science and Technology, COST B11, Quantitation of Magnetic Resonance Image Texture, 1998-2002 (last visited April 2010). http://phase.pki.uib.no/.
20. Brady M., Xie Z. Feature Selection for Texture Segmentation 1996, IEEE Computer Society Press, Los Alamitos, CA, pp. 28-44.
21. MaZda Web Site, (last update April 2010). http://www.eletel.p.lodz.pl/mazda/.
22. Szczypinski P., Strzelecki M., Materka A., Klepaczko A. MaZda - A software package for image texture analysis. Computer Methods and Programs in Biomedicine 2009, 94:66-76.
23. European Cooperation in Science and Technology, COST B21, Physiological Modelling of MR Image Formation, 2003-2007 (last visited April 2010). http://www.die.upm.es/costb21/frame_main.html.
24. D. Mahmoud-Ghoneim, Development of methods of texture analysis in magnetic resonance imaging: application on muscular and adipose tissues, Ph.D. thesis, University of Rennes, 2003 (in French).
25. Schürmann J. Pattern Classification: a Unified View of Statistical and Neural Approaches 1996, John Wiley & Sons, Inc., New York, NY, USA.
26. A.N. Mucciardi, E.E. Gose, A comparison of seven techniques for choosing subsets of pattern recognition properties, IEEE Transactions on Computers 9 (1971) 1023-1031.
27. Dash M., Liu H. Feature selection for classification. Intelligent Data Analysis 1997, 1:131-156.
28. Tourassi G.D., Frederick E.D., Markey M.K., Floyd C.E. Application of the mutual information criterion for feature selection in computer-aided diagnosis. Medical Physics 2001, 28(12):2394-2402.
29. Materka A., Strzelecki M., Lerski R., Schad L. Feature evaluation of texture test objects for magnetic resonance imaging. Machine Perception & Artificial Intelligence 2000, vol. 40:197-206. World Scientific. M.K. Pietikainen (Ed.).
30. Duda R., Hart P., Stork D. Pattern Classification 2000, Wiley-Interscience. 2nd ed.
31. Swets D.L., Weng J.J. Using discriminant eigenfeatures for image retrieval. Transactions on Pattern Analysis and Machine Intelligenc 1996, 18(8):831-836.
32. Dickie R., Bachoo R.M., Rupnick M.A., Dallabrida S.M., Deloid G.M., Lai J., Depinho R.A., Rogers R.A. Three-dimensional visualization of microvessel architecture of whole-mount tissue by confocal microscopy. Microvascular Research 2006, 72(1-2):20-26.
33. Chekenya M., Hjelstuen M., Enger P.O., Thorsen F., Jacob A.L., Probst B., Haraldseth O., Pilkington G., Butt A., Levine J.M., Bjerkvig R. NG2 proteoglycan promotes angiogenesis-dependent tumor growth in CNS by sequestering angiostatin. FASEB Journal 2002, 16(6):586-588.
34. Chekenya M., Enger P.O., Thorsen F., Tysnes B.B., Al-Sarraj S., Read T.A., Furmanek T., Mahesparan R., Levine J.M., Butt A.M., Pilkington G.J., Bjerkvig R. The glial precursor proteoglycan, NG2, is expressed on tumour neovasculature by vascular pericytes in human malignant brain tumours. Neuropathology and Applied Neurobiology 2002, 28(5):367-380.
35. Haralick R. Statistical and structural approaches to texture. IEEE Proceedings 1979, 768-804.
36. G. Ridgway, Rice/Rician distribution, 2007. http://www.mathworks.com/matlabcentral/.
37. A. Materka, M. Strzelecki, Texture Analysis Methods - A Review, COST B11 Report, 1998 (last visited 20.04.11). http://www.eletel.p.lodz.pl/programy/cost/publikacje.html.
38. Haralick R., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Transactions on System, Man, and Cybernetics 1973, 6(SCM-3):610-621.
39. Klepaczko A., Materka A. Clustering stability-based feature selection for unsupervised texture classification. Machine Graphics and Vision 2009, 18(2):125-141.
40. Klepaczko A., Kociński M., Materka A. Cluster analysis in application to quantitative inspection of 3D vascular tree images. Computer Recognition Systems, Proceedings of the 6th International Conference on Computer Recognition Systems, CORES'09, Advances in Intelligent and Soft Computing 2009, 87-94. Springer. M. Kurzynski, M. Wozniak (Eds.).
41. Klepaczko A., Kociński M., Materka A., Chekenya M., Lundervold A. Texture analysis in application to quantitative study of 3D vascular tree images. Proceedings of 6th International Symposium on Image and Signal Processing and Analysis. ISPA 2009 2009, 730-735.
42. Witten I.H., Frank E. Data Mining. Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann.