Discrimination between Alzheimer’s Disease, Mild Cognitive Impairment and Normal Aging Using ANN Based MR Brain Image Segmentation

  • Tinu Varghese
  • R. Sheela Kumari
  • P. S. Mathuranath
  • N. Albert Singh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


Alzheimer’s disease (AD) is the most common cause of dementia among people aged 60 years and older. Mild Cognitive impairment (MCI) is a pre-dementia condition that has been shown to have a high likelihood of progression to AD. In this prospective study evaluate the accuracy of the GM and CSF volumetry to help distinguish between patients with AD and MCI and subjects with elderly controls. This study we explored the ability of BP-ANN identify the structural changes of Grey Matter (GM), White Matter (WM) and Cerebrospinal fluid (CSF) in different groups using real MR images. The proposed approach employs morphological operations used for skull stripping and gabor filter for feature extraction. In these results we report a statistically significant trend towards accelerated GM volume loss in the MCI group compared to the NCI and AD from the MCI. We report the results of the classification accuracies on both training and test images are up to 96%.


Alzheimer’s disease Mild Cognitive Impairment No Cognitive impairment Artificial Neural network Magnetic Resonance Imaging 


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  1. 1.
    Rahul, S., Desikan, H.J.C.: Automated MRI measures identify individuals with MCI and AD. Brain 132, 2048–2057 (2009)CrossRefGoogle Scholar
  2. 2.
    Alistair, B., Michael, Z.: Mild cognitive impairment in older people. Lancet. 360, 1963–1965 (2002)CrossRefGoogle Scholar
  3. 3.
    Wattamwar, P.R., Mathuranath, P.S.: An overview of biomarkers in Alzheimer’s disease. Ann. Indian Acad. Neurol. 13, 116–123 (2010)CrossRefGoogle Scholar
  4. 4.
    Mathuranath, P.S., Mathew, R.: Role of subjective memory complaints in defing MCI. Neurobiology of Aging 25, 74–79 (2004)CrossRefGoogle Scholar
  5. 5.
    Barbra, R., Monica, N., Helle, W.: Investigating poor insight in AD: A survey research approaches. Dementia 6, 44–61 (2007)Google Scholar
  6. 6.
    Kannan, S.R., Sathya, A., Ramathilagam, S., Devi, R.: Novel segmentation algorithm in segmenting medical images. Journal of Systems and Software 8, 2487–2495 (2010)CrossRefGoogle Scholar
  7. 7.
    Luca, M.D., Grossi, E., Borroni, B., Zimmermann, M., Marcello, E., Colciaghi, F., Gardoni, F., Intraligi, M., Padovani, A., Buscema, M.: Artificial neural networks allow the use of simultaneous measurements of Alzheimer Disease markers for early detection of the disease. Journal of Translational Medicine 3, 30–39 (2005)CrossRefGoogle Scholar
  8. 8.
    Devanand, D.P., Liu, J., Hao, X., Pradhaban, G., Peterson, B.S.: MRI hippocampal and entorhinal cortex mapping in predicting conversion to AD. Neuroimage 60, 1622–1629 (2012)CrossRefGoogle Scholar
  9. 9.
    Reed, R.T., du Buf, J.M.H.: A review of recent texture segmentation and feature extraction techniques. Comput. Vis. Graphics Image Processing 57, 359–372 (1993)CrossRefGoogle Scholar
  10. 10.
    Yang, S.-T., Lee, J.-D., Huang, C.-H., Wang, J.-J., Hsu, W.-C., Wai, Y.-Y.: Computer-Aided Diagnosis of Alzheimer’s Disease Using Multiple Features with Artificial Neural Network. In: Zhang, B.-T., Orgun, M.A. (eds.) PRICAI 2010. LNCS, vol. 6230, pp. 699–705. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–646 (1994)CrossRefGoogle Scholar
  12. 12.
    Varghese, T., Kumari, R.S., Mathuranath, P.S., Albert Singh, N.: Performance Evaluation of Bacterial Foraging Optimization Algorithm for the Early Diagonosis and Tracking of Alzheimer’s Disease. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds.) SEMCCO 2012. LNCS, vol. 7677, pp. 41–48. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Deepa, S.N., Aruna Devi, B.: A survey on artificial intelligence approaches for medical image classification. Indian Journal of Science and Technology 4, 11 (2011)Google Scholar
  14. 14.
    Hojjatoleslami, S.A., Kittler, J.: Region Growing: A New Approach. IEEE Transactions on Image Processing 7, 7 (1998)CrossRefGoogle Scholar
  15. 15.
    Shanthi, K.J., Sasi Kumar, M., Kesavadas, C.: Neural Network Model for Automatic Segmentation of Brain MRI. IEEE (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tinu Varghese
    • 1
  • R. Sheela Kumari
    • 2
  • P. S. Mathuranath
    • 3
  • N. Albert Singh
    • 1
  1. 1.Noorul Islam UniversityThuckalayIndia
  2. 2.Sree Chitra Tirunal Institute for Medical Science and TechnologyTrivandrumIndia
  3. 3.Department of NeurologySree Chitra Tirunal Institute for Medical Science and TechnologyTrivandrumIndia

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