Isosurface Modelling of DatSCAN Images for Parkinson Disease Diagnosis

  • M. Martínez-Ibañez
  • A. OrtizEmail author
  • J. Munilla
  • Diego Salas-Gonzalez
  • J. M. Górriz
  • J. Ramírez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)


This paper proposes the computing of isosurfaces as a way to extract relevant features from 3D brain images. These isosurfaces are then used to implement a Computer aided diagnosis system to assist in the diagnosis of Parkinson’s Disease (PD) which uses a most well-known Convolutional Neural Networks (CNN) architecture, LeNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.


Deep learning Convolutional networks Isosurfaces Parkinson’s Disease 



This work was partly supported by the MINECO/FEDER under TEC2015-64718-R and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. PPMI - a public - private partnership - is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc.


  1. 1.
    Badoud, S., Ville, D.V.D., Nicastro, N., Garibotto, V., Burkhard, P.R., Haller, S.: Discriminating among degenerative parkinsonisms using advanced 123i-ioflupane SPECT analyses. NeuroImage: Clin. 12, 234–240 (2016)CrossRefGoogle Scholar
  2. 2.
    Bhalchandra, N.A., Prashanth, R., Roy, S.D., Noronha, S.: Early detection of Parkinson’s disease through shape based features from 123I-Ioflupane SPECT imaging. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 963–966, April 2015.
  3. 3.
    Brahim, A., Ramírez, J., Górriz, J., Khedher, L., Salas-Gonzalez, D.: Comparison between different intensity normalization methods in 123I-Ioflupane imaging for the automatic detection of Parkinsonism. PLoS One 10(6: e0130274), 1–20 (2015)CrossRefGoogle Scholar
  4. 4.
    Illán, I.A., Górriz, J.M., Ramírez, J., Segovia, F., Hoyuela, J.M.J., Lozano, S.J.O.: Automatic assistance to Parkinsons disease diagnosis in DaTSCAN SPECT imaging. Med. Phys. 39(10), 5971–5980 (2012). Scholar
  5. 5.
    Khedher, L., Ramírez, J., Górriz, J., Brahim, A., Segovia, F.: Early diagnosis of disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 151, 139–150 (2015). Scholar
  6. 6.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 1097–1105. Curran Associates Inc., USA (2012)Google Scholar
  7. 7.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  8. 8.
    Martinez-Murcia, F.J., Górriz, J.M., Ramírez, J., Moreno-Caballero, M., Gómez-Río, M.: Parametrization of textural patterns in 123I-Ioflupane imaging for the automatic detection of Parkinsonism. Med. Phys. 41(1) (2014)CrossRefGoogle Scholar
  9. 9.
    Martínez-Murcia, F., Górriz, J., Ramírez, J., Illán, I., Ortiz, A.: Automatic detection of Parkinsonism using significance measures and component analysis in DaTSCAN imaging. Neurocomputing 126, 58–70 (2014). Recent trends in Intelligent Data Analysis Online Data ProcessingCrossRefGoogle Scholar
  10. 10.
    Martinez-Murcia, F.J., Górriz, J.M., Ramírez, J., Ortiz, A.: Convolutional neural networks for neuroimaging in Parkinson’s disease: is preprocessing needed? Int. J. Neural Syst. (2018). Scholar
  11. 11.
    Martinez-Murcia, F.J., et al.: A 3D convolutional neural network approach for the diagnosis of Parkinson’s disease. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2017. LNCS, vol. 10337, pp. 324–333. Springer, Cham (2017). Scholar
  12. 12.
    London Institute of Neurology, UCL: Statistical parametrix mapping (2012).
  13. 13.
    Oliveira, F.P.M., Castelo-Branco, M.: Computer-aided diagnosis of Parkinson’s disease based on [(123)I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines. J. Neural Eng. 12(2) (2015). Scholar
  14. 14.
    Ortiz, A., Martínez-Murcia, F.J., García-Tarifa, M.J., Lozano, F., Górriz, J.M., Ramírez, J.: Automated diagnosis of Parkinsonian syndromes by deep sparse filtering-based features. In: Chen, Y.-W., Tanaka, S., Howlett, R.J., Jain, L.C. (eds.) Innovation in Medicine and Healthcare 2016. SIST, vol. 60, pp. 249–258. Springer, Cham (2016). Scholar
  15. 15.
    Ortiz, A., Munilla, J., Martinez-Murcia, F.J., Górriz, J.M., Ramírez, J.: Empirical functional PCA for 3D image feature extraction through fractal sampling. Int. J. Neural Syst. 1–22 (2019). Scholar
  16. 16.
    Palumbo, B., et al.: Diagnostic accuracy of Parkinson disease by support vector machine (SVM) analysis of 123I-FP-CIT brain SPECT data: implications of putaminal findings and age. Medicine 93(27), e228 (2014). Scholar
  17. 17.
    Palumbo, B., et al.: Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson’s disease by (123)I-FP-CIT brain SPECT. Eur. J. Nuclear Med. Mol. Imaging 37(11), 2146–2153 (2010). Scholar
  18. 18.
    Prashanth, R., Dutta Roy, S., Mandal, P.K., Ghosh, S.: Automatic classification and prediction models for early Parkinson’s disease diagnosis from SPECT imaging. Expert Syst. Appl. 41(7), 3333–3342 (2014). Scholar
  19. 19.
    Rojas, A., et al.: Application of empirical mode decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson disease. Expert Syst. Appl. 40(7), 2756–2766 (2013)CrossRefGoogle Scholar
  20. 20.
    Salas-Gonzalez, D., et al.: Building a FP-CIT SPECT brain template using a posterization approach. Neuroinformatics 13(4), 391–402 (2015)CrossRefGoogle Scholar
  21. 21.
    Segovia, F., Górriz, J.M., Ramírez, J., Chaves, R., Illán, I.Á.: Automatic differentiation between controls and Parkinson’s disease DaTSCAN images using a partial least squares scheme and the fisher discriminant ratio. In: KES, pp. 2241–2250 (2012)Google Scholar
  22. 22.
    Taylor, J.C., Fenner, J.W.: Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification? EJNMMI Phys. 4, 29 (2017). Scholar
  23. 23.
    Towey, D.J., Bain, P.G., Nijran, K.S.: Automatic classification of 123I-FP-CIT (DaTSCAN) SPECT images. Nuclear Med. Commun. 32(8), 699–707 (2011)CrossRefGoogle Scholar
  24. 24.
    Zhang, Y.C., Kagen, A.C.: Machine learning interface for medical image analysis. J. Digit. Imaging 30(5), 615–621 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • M. Martínez-Ibañez
    • 1
  • A. Ortiz
    • 1
    Email author
  • J. Munilla
    • 1
  • Diego Salas-Gonzalez
    • 2
  • J. M. Górriz
    • 2
  • J. Ramírez
    • 2
  1. 1.Communications Engineering DepartmentUniversity of MálagaMálagaSpain
  2. 2.Department of Signal Theory, Communications and NetworkingUniversity of GranadaGranadaSpain

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