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SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

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Abstract

Alzheimer's disease is the most frequent type of dementia for elderly patients. Due to aging populations, the occurrence of this disease will increase in the next years. Early diagnosis is crucial to be able to develop more powerful treatments. Brain perfusion changes can be a marker for Alzheimer's disease. In this article, we study the use of SPECT perfusion imaging for the diagnosis of Alzheimer's disease differentiating between images from healthy subjects and images from Alzheimer's disease patients. Our classification approach is based on a linear programming formulation similar to the 1-norm support vector machines. In contrast with other linear hyperplane-based methods that perform simultaneous feature selection and classification, our proposed formulation incorporates proximity information about the features and generates a classifier that does not just select the most relevant voxels but the most relevant “areas” for classification resulting in more robust classifiers that are better suitable for interpretation. This approach is compared with the classical Fisher linear discriminant (FLD) classifier as well as with statistical parametric mapping (SPM). We tested our method on data from four European institutions. Our method achieved sensitivity of 84.4% at 90.9% specificity, this is considerable better the human experts. Our method also outperformed the FLD and SPM techniques. We conclude that our approach has the potential to be a useful help for clinicians.

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References

  1. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Mental and clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of the Department of Health and Human Services Task Force on Alzheimer's disease. Neurology 34(7):939–944

    Google Scholar 

  2. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198

    Article  Google Scholar 

  3. Gosche KM, Mortimer JA, Smith CD, Markesbery WR, Snowdon DA (2002) Hippocampal volume as an index of Alzheimer neuropathology: findings from the Nun Study. Neurol 58(10):1476–1482

    Google Scholar 

  4. Goethals I, van de Wiele C, Slosman D, Dierckx R (2002) Brain SPET perfusion in early Alzheimer's disease: where to look? Eur J Nucl Med 29(8):975–978

    Article  Google Scholar 

  5. Claus JJ, van Harskamp F, Breteler MM, Krenning EP, de Koning I, van der Cammen TJ, Hofman A, Hasan D (1994) The diagnostic value of SPECT with Tc 99m HMPAO in Alzheimer's disease: a population-based study. Neurol 44(3):454–461

    Google Scholar 

  6. Messa C, Perani D, Lucignani G, Zenorini A, Zito F, Rizzo G, Grassi F, Del Sole A, Franceschi M, Gilardi MC, et al (1994) High-resolution technetium-99m-HMPAO SPECT in patients with probable Alzheimer's disease: comparison with fluorine-18-FDG PET. J Nucl Med 35(2):210–216

    Google Scholar 

  7. Jobst KA, Smith AD, Barker CS, Wear A, King EM, Smith A, Anslow PA, Molyneux AJ, Shepstone BJ, Soper N, et al (1992) Association of atrophy of the medial temporal lobe with reduced blood flow in the posterior parietotemporal cortex in patients with a clinical and pathological diagnosis of Alzheimer's disease. J Neurol Neurosurg Psychiatry 55(3):190–194

    Google Scholar 

  8. Talbot PR, Lloyd JJ, Snowden JS, Neary D, Testa HJ (1998) A clinical role for 99mTc-HMPAO SPECT in the investigation of dementia? J Neurol Neurosurg Psychiatry 64(3):306–313

    Google Scholar 

  9. Van Gool WA, Walstra GJ, Teunisse S, Van der Zant FM, Weinstein HC, Van Royen EA (1995) Diagnosing Alzheimer's disease in elderly, mildly demented patients: the impact of routine single photon emission computed tomography. J Neurol 242(6):401–405

    Article  Google Scholar 

  10. McMurdo ME, Grant DJ, Kennedy NS, Gilchrist J, Findlay D, McLennan JM (1994) The value of HMPAO SPECT scanning in the diagnosis of early Alzheimer's disease in patients attending a memory clinic. Nucl Med Commun 15(6):405–409

    Google Scholar 

  11. Kogure D, Matsuda H, Ohnishi T, Asada T, Uno M, Kunihiro T, Nakano S, Takasaki M (2000) Longitudinal evaluation of early Alzheimer's disease using brain perfusion SPECT. J Nucl Med 41(7):1155–1162

    Google Scholar 

  12. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol 42(1):85–94

    Article  Google Scholar 

  13. Ishii K, Sasaki M, Yamaji S, Sakamoto S, Kitagaki H, Mori E (1997) Demonstration of decreased posterior cingulate perfusion in mild Alzheimer's disease by means of H215O positron emission tomography. Eur J Nucl Med 24(6):670–673

    Google Scholar 

  14. Ibanez V, Pietrini P, Alexander GE, Furey ML, Teichberg D, Rajapakse JC, Rapoport SI, Schapiro MB, Horwitz B (1998) Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer's disease. Neurol 50(6):1585–1593

    Google Scholar 

  15. Braak H, Braak E (1997) Diagnostic criteria for neuropathologic assessment of Alzheimer's disease. Neurobiol Aging 18(4):S85–S88

    Article  Google Scholar 

  16. Bobinski M, de Leon MJ, Convit A, De Santi S, Wegiel J, Tarshish CY, Saint Louis LA, Wisniewski HM (1999) MRI of entorhinal cortex in mild Alzheimer's disease. Lancet 353(9146):38–40

    Article  Google Scholar 

  17. Rodriguez G, Vitali P, Calvini P, Bordoni C, Girtler N, Taddei G, Mariani G, Nobili F (2000) Hippocampal perfusion in mild Alzheimer's disease. Psychiatry Res 100(2):65–74

    Google Scholar 

  18. Dawson MR, Dobbs A, Hooper HR, McEwan AJ, Triscott J, Cooney J (1994) Artificial neural networks that use single-photon emission tomography to identify patients with probable Alzheimer's disease. Eur J Nucl Med 21(12):1303–1311

    Article  Google Scholar 

  19. Hamilton D, O'Mahony D, Coffey J, Murphy J, O'Hare N, Freyne P, Walsh B, Coakley D (1997) Classification of mild Alzheimer's disease by artificial neural network analysis of SPET data. Nucl Med Commun 18(9):805–810

    Article  Google Scholar 

  20. Frackowiak RSJ, Friston KJ, Frith CD, Dolan R (1997) Human brain function. Academic Press

  21. Ashburner J, Friston K, Holmes A, Poline J-B (1999) Statistical parametric mapping, SPM'99. The Welcome Department of Cognitive Neurol, Institute of Neurol, University College London. Freely available at: http://www.fil.ion.ucl.ac.uk/spm

  22. Stoeckel J, Malandain G, Migneco O, Koulibaly PM, Robert P, Ayache N, Darcourt J (2001) Classification of SPECT images of normal subjects versus images of Alzheimer's disease patients. In: Niessen WJ, Viergever MA (eds) Proceedings of the 4th international conference on medical image computing and computer-assisted intervention (MICCAI'01), Utrecht, The Netherlands. Lecture notes in computer science, vol 2208, pp 666–674

  23. Brown LG (1992) A survey of image registration techniques. ACM Comput Surveys 24(4):325–376

    Article  Google Scholar 

  24. van den Elsen PA, Pol EJD, Viergever MA (1993) Medical image matching—a review with classification. IEEE Eng Med Biol 12(4):26–39

    Article  Google Scholar 

  25. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–37

    Article  Google Scholar 

  26. Hill DL, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46(3):R1–R45

    Article  Google Scholar 

  27. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity metric for multimodal image registration. In: Wells WM, Colchester ACF, Delp S (eds) Medical image computing and computer-assisted intervention (MICCAI'98), Boston, USA. Lecture notes in computer science, vol 1496, pp 1115–1124

  28. Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1997) Numerical recipes. The art of scientific computing, 2nd edn. Cambridge University Press

  29. Prima S, Ourselin S, Ayache N (2002) Computation of the mid-sagittal plane in 3D brain images. IEEE Trans Med Image 21(2):122–138

    Article  Google Scholar 

  30. Yonekura Y, Nishizawa S, Mukai T, Fujita T, Fukuyama H, Ishikawa M, Kikuchi H, Konishi J, Andersen AR, Lassen NA (1988) SPECT with [99mTc]-d, l-hexamethyl-propylene amine oxime (HM-PAO) compared with regional cerebral blood flow measured by PET: effects of linearization. J Cereb Blood Flow Metab 8(6):S82–S89

    Google Scholar 

  31. Schmidt K (2002) Against: can ROI methodology/normalised tissue activities be used instead of absolute blood flow measurements in the brain? Eur J Nucl Med 29(7):953–956

    Article  Google Scholar 

  32. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  33. Bradley PS, Mangasarian OL (1998) Feature selection via concave minimization and support vector machines. In: Shavlik J (ed) Machine learning proceedings of the fifteenth international conference(ICML '98), San Francisco, CA. Morgan Kaufmann, pp 82–90. Available at: ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-03.ps

    Google Scholar 

  34. Mangasarian OL (1999) Arbitrary-norm separating plane. Oper Res Lett 24:15–23. Available at: ftp://ftp.cs.wisc.edu/math-prog/tech-reports/97-07r.ps

    Article  MATH  MathSciNet  Google Scholar 

  35. Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines. Adv Comput Math 13:1–50

    Article  MATH  MathSciNet  Google Scholar 

  36. Smola A, Bartlett PL, Schölkopf B, Schuurmann J (eds) (2000) Advances in large margin classifiers. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  37. Soonawala D, Amin T, Ebmeier KP, Steele JD, Dougall NJ, Best J, Migneco O, Nobili F, Scheidhauer K (2002) Statistical parametric mapping of (99m)Tc-HMPAO-SPECT images for the diagnosis of Alzheimer's disease: normalizing to cerebellar tracer uptake. Neuroimage 17(3):1193–1202

    Article  Google Scholar 

  38. Mika S, Rätsch G, Müller K-R (2000) A mathematical programming approach to the kernel fisher algorithm. NIPS, pp 591–597

  39. ILOG CPLEX Division, 889 Alder Avenue, Incline Village, Nevada. CPLEX Optimizer, 2004. http://www.cplex.com/+

    Google Scholar 

  40. Grova C, Janin P, Biraben A, Buvat I, Benali H, Bernard AM, Scarabin JM, Gibaud B (2001) Validation of MRI/SPECT similarity based registration methods using realistic simulations of normal and pathological data. In: Niessen WJ, Viergever MA (eds) Medical image computing and computer-assisted intervention (MICCAI'01). Lecture notes in computer science, vol 2208, pp 257–282

  41. Grova C (2002) Simulations réalistes de donées de tomographie monophotonique (TEMP) pour l'évaluation de methodes de recalage TEMP/IRM utilisant des mesures de statistiques de similarité: application dans le contexte de la fusion de données en épilepsie. PhD thesis, Université de Rennes I

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  1. Glenn Fung

    Present address: Computer Aided Diagnosis & therapy group, 51 valley Stream Parkway, Nalvern, PA, USA

Authors and Affiliations

  1. Siemens Medical Solutions USA, Computer Aided Diagnosis, Malvern, PA, 19355, USA

    Glenn Fung & Jonathan Stoeckel

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  1. Glenn Fung
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  2. Jonathan Stoeckel
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Correspondence to Glenn Fung.

Additional information

Glenn Fung received a B.S. degree in pure mathematics from Universidad Lisandro Alvarado in Barquisimeto, Venezuela, then earned an M.S. in applied mathematics from Universidad Simon Bolivar, Caracas, Venezuela, where later he worked as an assistant professor for 2 years. Later, he earned an M.S. degree and a Ph.D. degree in computer sciences from the University of Wisconsin-Madison. His main interests are optimization approaches to machine learning and data mining, with emphasis in support vector machines. In the summer of 2003, he joined the computer aided diagnosis group at Siemens, Medical Solutions in Malvern, PA, where he has been applying machine learning techniques to solve challenging problems that arise in the medical domain. His recent papers are available at www.cs.wisc.edu/gfung.

Jonathan Stoeckel received a B.E. degree from Xi’an Jiao Tong University, Xi'an, China, in 1993 and an M.E. degree from Shanghai Jiao Tong University, Shanghai, China, in 1996. From 1997 to 1998, he did research work in the Data Mining Group at the School of Computing and Information Technology, Griffith University, Brisbane, Australia. He is currently a Ph.D. student at the Department of Computer Science, Dartmouth College, USA. His research interests include data mining, multimedia, database and software engineering.

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Fung, G., Stoeckel, J. SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information. Knowl Inf Syst 11, 243–258 (2007). https://doi.org/10.1007/s10115-006-0043-5

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