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.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
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)
Coupé, P., et al.: Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. NeuroImage: clin. 1(1), 141–152 (2012)
Dukart, J., et al.: Age correction in dementia-matching to a healthy brain. PLoS One 6(7), e22193 (2011)
Giraud, R., et al.: An optimized patchmatch for multi-scale and multi-feature label fusion. NeuroImage 124, 770–782 (2016)
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
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
Liu, M., et al.: Ensemble sparse classification of Alzheimer’s disease. NeuroImage 60(2), 1106–1116 (2012)
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)
Tong, T., et al.: Multiple instance learning for classification of dementia in brain MRI. Med. Image Anal. 18(5), 808–818 (2014)
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)
Wolz, R., et al.: Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6(10), e25446 (2011)
Manjón, J.V., Coupé, P.: volBrain: an online MRI brain volumetry system. Front. neuroinformatics, 10, 2016
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)
Suk, H.I., et al.: Deep ensemble learning of sparse regression models for brain disease diagnosis. Med. Image Anal. 37, 101–113 (2017)
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).
Author information
Authors and Affiliations
Consortia
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Hett, K., Ta, VT., Manjón, J.V., Coupé, P., the Alzheimer’s Disease Neuroimaging Initiative. (2017). Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-67434-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67433-9
Online ISBN: 978-3-319-67434-6
eBook Packages: Computer ScienceComputer Science (R0)