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Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection

  • Kilian HettEmail author
  • Vinh-Thong Ta
  • José V. Manjón
  • Pierrick Coupé
  • the Alzheimer’s Disease Neuroimaging Initiative
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

Abstract

Alzheimer’s disease is a neurodegenerative process leading to irreversible mental dysfunctions. The development of new biomarkers is crucial to perform an early detection of this disease. Among new biomarkers proposed during the last decades, patch-based grading framework demonstrated state-of-the-art results. In this paper, we study the potential using texture information based on Gabor filters to improve patch-based grading method performance, with a focus on the hippocampal structure. We also propose a novel fusion framework to efficiently combine multiple grading maps derived from a Gabor filters bank. Finally, we compare our new texture-based grading biomarker with the state-of-the-art approaches to demonstrate the high potential of the proposed method.

Keywords

Patch-based grading fusion Multi-features Alzheimer’s disease classification Mild Cognitive Impairment 

Notes

Acknowledgement

This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HL-MRI ANR-10-LABX-57).

References

  1. 1.
    Bron, E.E., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage 111, 562–579 (2015)CrossRefGoogle Scholar
  2. 2.
    Coupé, P., et al.: Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage: clin. 1(1), 141–152 (2012)CrossRefGoogle Scholar
  3. 3.
    Dukart, J., et al.: Age correction in dementia-matching to a healthy brain. PLoS One 6(7), e22193 (2011)CrossRefGoogle Scholar
  4. 4.
    Giraud, R., et al.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)CrossRefGoogle Scholar
  5. 5.
    Hett, K., et al.: Patch-based DTI grading: application to Alzheimer’s disease classification. In: Wu, G., Coupé, P., Zhan, Y., Munsell, B.C., Rueckert, D. (eds.) Patch-MI 2016. LNCS, vol. 9993, pp. 76–83. Springer, Cham (2016). doi: 10.1007/978-3-319-47118-1_10 CrossRefGoogle Scholar
  6. 6.
    Komlagan, M., et al.: Anatomically constrained weak classifier fusion for early detection of Alzheimer’s disease. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 141–148. Springer, Cham (2014). doi: 10.1007/978-3-319-10581-9_18 Google Scholar
  7. 7.
    Liu, M., et al.: Ensemble sparse classification of Alzheimer’s disease. NeuroImage 60(2), 1106–1116 (2012)CrossRefGoogle Scholar
  8. 8.
    Sørensen, L., et al.: Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. NeuroImage: Clin. 13, 470–482 (2016)CrossRefGoogle Scholar
  9. 9.
    Tong, T., et al.: Multiple instance learning for classification of dementia in brain MRI. Med. Image Anal. 18(5), 808–818 (2014)CrossRefGoogle Scholar
  10. 10.
    Tong, T., et al.: A novel grading biomarker for the prediction of conversion from mild cognitive impairment to Alzheimer’s disease. IEEE Trans. Biomed. Eng. 64(1), 155–165 (2017)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Wolz, R., et al.: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6(10), e25446 (2011)CrossRefGoogle Scholar
  12. 12.
    Manjón, J.V., Coupé, P.: volBrain: an online MRI brain volumetry system. Front. neuroinformatics, 10, 2016Google Scholar
  13. 13.
    Manjunath, B.S., et al.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)CrossRefGoogle Scholar
  14. 14.
    Suk, H.I., et al.: Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal. 37, 101–113 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kilian Hett
    • 1
    • 2
    Email author
  • Vinh-Thong Ta
    • 1
    • 2
    • 3
  • José V. Manjón
    • 4
  • Pierrick Coupé
    • 1
    • 2
  • the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Univ. Bordeaux, LaBRI, UMR 5800, PICTURATalenceFrance
  2. 2.CNRS, LaBRI, UMR 5800, PICTURATalenceFrance
  3. 3.Bordeaux INP, LaBRI, UMR 5800, PICTURAPessacFrance
  4. 4.ITACAUniversitat Politècnia de ValènciaValenciaSpain

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