Intensity normalization of DaTSCAN SPECT imaging using a model-based clustering approach

Applied Soft Computing Journal

Volumn 37, Issue , 2015, Pages 234-244

  Brahim, A ^a   Gorriz J M ^a   Ramirez J ^a   Khedher, L ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Computer aided diagnosis (CAD); DaTSCAN SPECT images; Gaussian mixture models; Intensity normalization; Parkinson's disease

Indexed keywords

ALGORITHMS; BRAIN MAPPING; DIAGNOSIS; GAUSSIAN DISTRIBUTION; MAXIMUM LIKELIHOOD; MAXIMUM PRINCIPLE; SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY;

COMPUTER AIDED DIAGNOSIS(CAD); GAUSSIAN MIXTURE MODEL; INTENSITY NORMALIZATION; PARKINSON'S DISEASE; SPECT IMAGES;

COMPUTER AIDED DIAGNOSIS;

EID: 84940735200     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.08.030     Document Type: Article

References (55)

1. J. Booij, G. Tissingh, G.J. Boer, J.D. Speelman, J.C. Stoof, A.G.M. Janssen, E.C. Wolters, and E.A. Van Royen [123I]FP-CIT SPECT shows a pronounced decline of striatal dopamine transporter labelling in early and advanced Parkinson's disease J. Neurol. Neurosurg. Psychiatry 62 1997 133 140
2. K.D. Seifert, and J.I. Wiener The impact of DaTscan on the diagnosis and management of movement disorders: a retrospective study Am. J. Neurodegener. Dis. 2 1 2013 29 34
3. A. Winogrodzka, P. Bergmans, J. Booij, E. van Royen, J. Stoof, and E. Wolters [123I]Beta-CIT SPECT is a useful method for monitoring dopaminergic degeneration in early stage Parkinson's disease Neurol. Neurosurg. Psychiatry 74 3 2003 294 298
4. I.G. Zubal, M. Early, O. Yuan, D. Jennings, K. Marek, and J. Seibyl Optimized, automated striatal uptake analysis applied to SPECT brain scans of Parkinson's disease patients J. Nuclear Med. 48 6 2007 857 864
5. A.S. Castrejón, A.M.G. Vicente, M.C. Romera, J.V. Cano, S.R. Marina, V.M.P. Garca, S.R. Sols, and M.P.T. Rubio 123-I ioflupane (Datscan) presynaptic nigrostriatal imaging in patients with movement disorders Braz. Archiv. Biol. Technol. 48 2005 115 126
6. R.A. Hauser, and D.G. Grosset [123I]FP-CIT (DaTscan) SPECT brain imaging in patients with suspected Parkinsonian syndromes Neuroimaging 22 3 2012 225 230
7. F. Segovia, J. Górriz, J. Ramírez, D. Salas-Gonzalez, I. Álvarez, M. López, and R. Chaves A comparative study of feature extraction methods for the diagnosis of Alzheimer's disease using the ADNI database Neurocomputing 75 1 2012 64 71
8. I.A. Illán, J.M. Górriz, J. Ramírez, F. Segovia, J.M. Jimenez-Hoyuela, and S.J.O. Lozano Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging Med. Phys. 39 10 2012 5971 5980
9. W. Reiche, M. Grundmann, and G. Huber Dopamine (D2) receptor SPECT with 123I-iodobenzamide (IBZM) in diagnosis of Parkinson syndrome Der Radiologe 35 1995 838 843
10. S. Bao, J. Wu, W. Luo, P. Fang, Z. Liu, and J. Tang Imaging of dopamine transporters with technetium-99m TRODAT-1 and single photon emission computed tomography J. Neuroimaging: Off. J. Am. Soc. Neuroimaging 10 2000 200 203
11. J. Booij, J. Habraken, P. Bergmans, G. Tissingh, A. Winogrodzka, E. Wolters, A. Janssen, J. Stoof, and E. Van Royen Imaging of dopamine transporters with Iodine-123-F-CIT SPECT in healthy controls and patients with Parkinson's disease J. Nuclear Med. 39 11 1998 1879 1884
12. K. Marek, R. Innis, C. van Dyck, B. Fussell, M. Early, S. Eberly, D. Oakes, and J. Seibyl [123I]β-CIT SPECT imaging assessment of the rate of Parkinson's disease progression Neurology 57 11 2001 2089 2094
13. M. Laruelle, E. Wallace, J. Seibyl, R. Baldwin, Y. Zea-Ponce, S. Zoghbi, J. Neumeyer, D. Charney, P. Hoffer, and R. Innis Graphical, kinetic and equilibrium analyses of in vivo [123I] β-CIT binding to dopamine transporters in healthy human subjects Int. Soc. Cereb. Blood Flow Metab. 14 1994 982 994
14. C. Scherfler, K. Seppi, E. Donnemiller, G. Goebel, C. Brenneis, I. Virgolini, G. Wenning, and W. Poewe Voxel-wise analysis of [123I]β-CIT SPECT differentiates the Parkinson variant of multiple system atrophy from idiopathic Parkinson's disease Brain 128 7 2005 1605 1612
15. V. Caretti, D. Stoffers, A. Winogrodzka, I.-U. Isaias, G. Costantino, G. Pezzoli, C. Ferrarese, A. Antonini, E.-C. Wolters, and J. Booij Loss of thalamic serotonin transporters in early drug-naïve Parkinson's disease patients is associated with tremor: an [123I]β-CIT SPECT study Neural Transm. 115 5 2008 721 729
16. S. Sharma, and M. Ebadi SPECT neuroimaging in translational research of CNS disorders Neurochem. Int. 52 3 2008 352 362
17. K.J. Friston, C.D. Frith, P.F. Liddle, R.J. Dolan, A.A. Lammertsma, and R.S.J. Frackowiak The relationship between global and local changes in PET scans J. Cereb. Blood Flow Metab. 10 1990 458 466
18. H.T.S. Benamer, J. Patterson, and D.G. Grosset Accurate differentiation of Parkinsonism and essential tremor using visual assessment of [123I]-FP-CIT SPECT imaging: the [123I]-FP-CIT study group Mov. Disorders 15 3 2000 503 510
19. L. Tossici-Bolt, S.M.A. Hoffmann, P.M. Kemp, R.L. Mehta, and J.S. Fleming Quantification of [123I]FP-CIT SPECT brain images: an accurate technique for measurement of the specific binding ratio Eur. J. Nuclear Med. Mol. Imaging 33 2006 1491 1499
20. A. Brahim, J. Górriz, J. Ramírez, and L. Khedher Linear intensity normalization of DaTSCAN images using mean square error and a model-based clustering approach Stud. Health Technol. Inform. 207 2014 251 260
21. A. Brahim, J. Górriz, J. Ramírez, and L. Khedher Intensity normalization of 123I-ioflupane-spect brain images using a model-based multivariate linear regression approach J.M. Ferrández Vicente, J.R. Álvarez Sánchez, F. de la Paz López, F.J. Toledo-Moreo, H. Adeli, Artificial Computation in Biology and Medicine, Vol. 9107 of Lecture Notes in Computer Science 2015 Springer International Publishing 68 77
22. M. Shah, Y. Xiao, N. Subbanna, S. Francis, L. Arnold, D. Collins, and T. Arbel Evaluating intensity normalization on MRIs of human brain with multiple sclerosis Med. Image Anal. 15 2011 267 282
23. P. Borghammer, J. Aanerud, and A. Gjedde Data-driven intensity normalization of PET group comparison studies is superior to global mean normalization NeuroImage 46 2009 981 988
24. C. Gullion, M.S. Devous, and A. Rush Effects of four normalizing methods on data analytic results in functional brain imaging Biol. Psychiatry 40 1996 1106 1121
25. S. Arndt, T. Cizadlo, D. O'Leary, S. Gold, and N. Andreasen Normalizing counts and cerebral blood flow intensity in functional imaging studies of the human brain NeuroImage 3 1996 175 184
26. T. Eckert, A. Barnes, V. Dhawan, S. Frucht, M. Gordon, A. Feigin, and D. Eidelberg FDG PET in the differential diagnosis of Parkinsonian disorders NeuroImage 26 2005 912 921
27. D. Salas-Gonzalez, J.M. Górriz, J. Ramírez, P. Padilla, and I.A. Illán Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization Comput. Math. Methods Med. 2013 2013 1 8
28. D. Salas-Gonzalez, J.M. Górriz, J. Ramírez, I.A. Illán, and E.W. Lang Linear intensity normalization of FP-CIT SPECT brain images using the α-stable distribution NeuroImage 65 2013 449 455
29. W. Pirker, S. Asenbaum, G. Bencsits, D. Prayer, W. Gerschlager, L. Deecke, and T. Brücke [123I]β-CIT SPECT in multiple system atrophy, progressive supranuclear palsy, and corticobasal degeneration Mov. Disorders 15 2000 1158 1167
30. M. Lyra, and A. Ploussi Filtering in SPECT image reconstruction Int. J. Biomed. Imaging 2011 2011 14
31. K. Van Laere, M. Koole, I. Lemahieu, and R. Dierckx Image filtering in single-photon emission computed tomography: principles and applications Comput. Med. Imaging Graph. 25 2 2001 127 133
32. K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny Statistical Parametric Mapping: The Analysis of Functional Brain Images 2007 Academic Press
33. S.J.O. Lozano, M.D.M. Del Valle Torres, J.M. Jiménez-Hoyuela García, A.L.G. Cardo, and V.C. Arillo Diagnostic accuracy of FP-CIT SPECT in patients with Parkinsonism Rev. Esp. Med. Nucl. (Engl. Ed.) 26 2007 277 285
34. K.S. Tamilselvan, G. Murugesan, and M. Vinothsaravanan A histogram based hybrid approach for medical image denoising using wavelet and curvelet transforms Int. J. Comput. Appl. 74 21 2013 6 11
35. S.C. Liew, B.H. Hasegawa, J.K. Brown, and T.F. Lang Noise propagation in SPECT images reconstructed using an iterative maximum-likelihood algorithm Phys. Med. Biol. 38 1993 1713 1726
36. J. Ashburner, and K. Friston Unified segmentation NeuroImage 26 3 2005 839 851
37. C. la Fougere, G. Pöpperl, J. Levin, B. Wängler, G. Böning, C. Uebleis, P. Cumming, P. Bartenstein, K. Bötzel, and K. Tatsch The value of the dopamine D2/3 receptor ligand 18F-desmethoxyfallypride for the differentiation of idiopathic and nonidiopathic Parkinsonian syndromes J. Nuclear Med. 51 4 2010 581 587
38. K. Fukunaga Introduction to Statistical Pattern Recognition 1990 Academic Press New York
39. J. Goldberger, S. Gordon, and H. Greenspan Unsupervised image-set clustering using an information theoretic framework IEEE Trans. Image Process. 15 2 2006 449 458
40. G. McLachlan, and D. Peel Finite Mixture Models 2000 John Wiley & Sons New York
41. M. Aladjem Projection pursuit mixture density estimation IEEE Trans. Signal Process. 53 11 2005 4376 4383
42. J.P. Nolan Levy Processes: Theory and Applications. Maximum Likelihood Estimation of Stable Parameters 2001 Birkhäuser Boston 379 400
43. J. Neumann, D.Y.V. Cramon, and G. Lohmann Model-based clustering of meta-analytic functional imaging data Hum. Brain Mapp. 29 2 2008 177 192
44. G.J. McLachlan, and P.N. Jones Fitting mixture models to grouped and truncated data via the EM algorithm Biometrics 44 1988 571 578
45. J.M. Górriz, F. Segovia, J. Ramírez, A. Lassl, and D. Salas-Gonzalez GMM based SPECT image classification for the diagnosis of Alzheimers disease Appl. Soft Comput. 11 2 2011 2313 2325
46. T. Moon The expectation-maximization algorithm IEEE Signal Process. Mag. 13 1996 47 60
47. A. Dempster, N. Laird, and D. Rubin Maximum likelihood from incomplete data via the EM algorithm J. R. Stat. Soc. 39 1 1977 1 38
48. J.M. Górriz, A. Lassl, J. Ramírez, D. Salas-Gonzalez, C. Puntonet, and E. Lang Automatic selection of ROIs in functional imaging using Gaussian mixture models Neurosci. Lett. 460 2009 108 111
49. G. E. Healthcare A Technologist's Guide to DaTSCAN Imaging, Tech. Rep. 2007 Available from: http://www.datscan.cz/stranky/dokumenty-ke-stazeni/firemni-materialy-ge-healthcare.html
50. F. Segovia, J.M. Górriz, J. Ramírez, D. Salas-González, I.A. Illán, M. López, R. Chaves, P. Padilla, and C. Puntonet Automatic selection of ROIs using a model-based clustering approach 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC) 2009 3194 3198
51. F. Segovia, J.M. Górriz, J. Ramírez, D. Salas-González, I.A. Illán, M. López, R. Chaves, and P. Padilla Classification of functional brain images using a GMM-based multi-variate approach Neurosci. Lett. 474 2010 58 62
52. H. Greenspan, A. Ruf, and J. Goldberger Constrained Gaussian mixture model framework for automatic segmentation of MR brain images IEEE Trans. Med. Imaging 25 9 2006 1233 1245
53. O. Freifeld, H. Greenspan, and J. Goldberger Multiple sclerosis lesion detection using constrained GMM and curve evolution Int. J. Biomed. Imaging 2009 2009 1 13
54. J. Stoeckel, N. Ayache, G. Malandain, P.M. Koulibaly, K. Ebmeier, and J. Darcourt Automatic classification of SPECT images of Alzheimer's disease patients and control subjects Lect. Notes Comput. Sci. 3217 2004 654 662
55. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Mining Knowl. Discov. 2 2 1998 121 167