Skip to main content
SpringerLink
Log in

Combining Static and Dynamic Features to Improve Longitudinal Image Retrieval for Alzheimer’s Disease

  • Conference paper
  • First Online:
ICT Innovations 2022. Reshaping the Future Towards a New Normal (ICT Innovations 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1740))

Included in the following conference series:

  • 244 Accesses

Abstract

The aim of this paper is to enhance medical case retrieval for Alzheimer’s disease on the basis of the domain knowledge. We approached the problem in a longitudinal manner, and we represented the medical cases by using different kind of information extracted from Magnetic Resonance Images (MRI) aiming to improve the semantic relevance, precision and efficiency of the retrieval. More particularly, we evaluated the combination of the static, dynamic features and the index reflecting the spatial pattern of abnormality (SPARE-AD) for representing the longitudinal images. According to the obtained results, the combination of the static features representing the volumetric measures along with the cortical thickness measures of the brain structures at the later time point/s together with the dynamic features such as percent change with respect to the value obtained from the linear fit at baseline and symmetrized percent change of the volumetric measures, as well as the index of abnormality provided the best overall retrieval results. The dimensionality of the feature vector was 31–33 features in most of the cases which is significantly lower than in the case of the traditional approach (thousands features in the cases when the whole brain is considered). The approach based on a combination of different kinds of features extracted from the longitudinal data, suggested in this paper, corresponds directly to the nature of the application domain and provides powerful results, yet effective and efficient way for MRI retrieval for AD.

“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 (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

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alzheimer’s Association, 2022. 2022 Alzheimer’s disease facts and figures. https://www.alz.org/media/documents/alzheimers-facts-and-figures.pdf. Accessed 30 June 2022

  2. Porsteinsson, A.P., Isaacson, R.S., Knox, S., Sabbagh, M.N., Rubino, I.: Diagnosis of early Alzheimer’s disease: clinical practice in 2021. J. Prev. Alzheimer’s Dis. 8(3), 371–386 (2021). https://doi.org/10.14283/jpad.2021.23

    Article  Google Scholar 

  3. Winblad, B., et al.: Defeating Alzheimer’s disease and other dementias: a priority for European science and society. The Lancet Neurol. 15(5), 455–532 (2016)

    Article  Google Scholar 

  4. Beason-Held, L.L., et al.: Changes in brain function occur years before the onset of cognitive impairment. J. Neurosci. 33(46), 18008–18014 (2013)

    Article  Google Scholar 

  5. Sperling, R.A., et al.: Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dement. 7(3), 280–292 (2011)

    Google Scholar 

  6. Wilson, R.S., Leurgans, S.E., Boyle, P.A., Bennett, D.A.: Cognitive decline in prodromal Alzheimer disease and mild cognitive impairment. Arch. Neurol. 68(3), 351–356 (2011)

    Article  Google Scholar 

  7. Agarwal, M., Mostafa, J.: Content-based image retrieval for Alzheimer’s disease detection. In: 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 13–18. IEEE (2011)

    Google Scholar 

  8. Cai, W., Liu, S., Wen, L., Eberl, S., Fulham, M.J., Feng, D.: 3D neurological image retrieval with localized pathology-centric CMRGlc patterns. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3201–3204. IEEE (2010)

    Google Scholar 

  9. Mizotin, M., Benois-Pineau, J., Allard, M., Catheline, G.: Feature-based brain MRI retrieval for Alzheimer disease diagnosis. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 1241–1244. IEEE (2012)

    Google Scholar 

  10. Liu, X., Chen, K., Wu, T., Weidman, D., Lure, F., Li, J.: Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer’s disease. Transl. Res. 194, 56–67 (2018)

    Article  Google Scholar 

  11. Trojacanec, K., Kitanovski, I., Dimitrovski, I., Loshkovska, S.: Medical image retrieval for Alzheimer’s disease using data from multiple time points. In: Loshkovska, S., Koceski, S. (eds.) International Conference on ICT Innovations, vol. 399, pp. 215–224. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25733-4_22

  12. Trojachanec, K., Kitanovski, I., Dimitrovski, I., Loshkovska, S.: Longitudinal brain MRI retrieval for Alzheimer’s disease using different temporal information. IEEE Access 6, 9703–9712 (2017)

    Article  Google Scholar 

  13. Trojacanec, K., Kalajdziski, S., Kitanovski, I., Dimitrovski, I., Loshkovska, S. and Alzheimer’s Disease Neuroimaging Initiative. Image retrieval for Alzheimer’s disease based on brain atrophy pattern. In: Trajanov, D., Bakeva, V. (eds.) International Conference on ICT Innovations, vol. 778, pp. 165–175. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67597-8_16

  14. Leon, R.A.M., Puentes, J., González, F.A., Hoyos, M.H.: Empirical evaluation of general-purpose image features for pathology-oriented image retrieval of Alzheimer disease cases. In: CARS 2016: 30th International Congress on Computer Assisted Radiology and Surgery (2016). Int. J. Comput. Assist. Radiol. Surg. 11, S39–S40

    Google Scholar 

  15. Chethan, K., Bhandarkar, R.: Hybrid feature extraction technique on brain MRI images for content-based image retrieval of Alzheimer’s disease. In: Kalya, S., Kulkarni, M., Shivaprakasha, K.S. (eds.) Advances in Communication, Signal Processing, VLSI, and Embedded Systems. LNEE, vol. 614, pp. 127–141. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0626-0_11

    Chapter  Google Scholar 

  16. Vinutha, N., Sandeep, S., Kulkarni, A.N., Shenoy, P.D., Venugopal, K.R.: A texture based image retrieval for different stages of Alzheimer’s disease. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), pp. 1–5. IEEE (2019)

    Google Scholar 

  17. Sagayam, K.M., Bruntha, P.M., Sridevi, M., Sam, M.R., Kose, U., Deperlioglu, O.: A cognitive perception on content-based image retrieval using an advanced soft computing paradigm. In: Advanced Machine Vision Paradigms for Medical Image Analysis, pp. 189–211. Academic Press (2021)

    Google Scholar 

  18. Kruthika, K.R., Maheshappa, H.D. and Alzheimer’s Disease Neuroimaging Initiative: Multistage classifier-based approach for Alzheimer’s disease prediction and retrieval. Inform. Med. Unlocked 14, 34–42 (2019)

    Google Scholar 

  19. Kruthika, K.R., Maheshappa, H.D. and Alzheimer’s Disease Neuroimaging Initiative: CBIR system using capsule networks and 3D CNN for Alzheimer’s disease diagnosis. Inform. Med. Unlocked 14, 59–68 (2019)

    Google Scholar 

  20. Veitch, D.P., et al.: Understanding disease progression and improving Alzheimer’s disease clinical trials: recent highlights from the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement. 15(1), 106–152 (2019)

    Article  MathSciNet  Google Scholar 

  21. Imtiaz, B., Tolppanen, A.M., Kivipelto, M., Soininen, H.: Future directions in Alzheimer’s disease from risk factors to prevention. Biochem. Pharmacol. 88(4), 661–670 (2014)

    Article  Google Scholar 

  22. Chatterjee, P., et al.: Diagnostic and prognostic plasma biomarkers for preclinical Alzheimer’s disease. Alzheimers Dement. 18(6), 1141–1154 (2022)

    Article  Google Scholar 

  23. Simrén, J., et al.: The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. Alzheimers Dement. 17(7), 1145–1156 (2021)

    Article  Google Scholar 

  24. Nanni, L., et al.: Comparison of transfer learning and conventional machine learning applied to structural brain MRI for the early diagnosis and prognosis of Alzheimer’s disease. Front. Neurol. 11, 576194 (2020)

    Google Scholar 

  25. Wang, Y., et al.: Diagnosis and prognosis of Alzheimer’s disease using brain morphometry and white matter connectomes. NeuroImage: Clin. 23, 101859 (2019)

    Google Scholar 

  26. Salvatore, C., Castiglioni, I.: A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer’s disease. J. Neurosci. Methods 302, 58–65 (2018)

    Article  Google Scholar 

  27. Soininen, H., et al.: 36‐month LipiDiDiet multinutrient clinical trial in prodromal Alzheimer’s disease. Alzheimer’s & Dement. 17(1), 29–40 (2021)

    Google Scholar 

  28. Meyer, P.F., et al.: INTREPAD: a randomized trial of naproxen to slow progress of presymptomatic Alzheimer disease. Neurology 92(18), e2070–e2080 (2019)

    Google Scholar 

  29. Rebsamen, M., Suter, Y., Wiest, R., Reyes, M., Rummel, C.: Brain morphometry estimation: from hours to seconds using deep learning. Front. Neurol. 11, 244 (2020)

    Article  Google Scholar 

  30. Alzheimer’s Disease Neuroimaging Initiative: ADNI (2017). https://adni.loni.usc.edu/. Accessed 30 June 2022

  31. FreeSurfer. https://surfer.nmr.mgh.harvard.edu/. Accessed 17 June 2022

  32. Han, X., et al.: Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32(1), 180–194 (2006)

    Article  Google Scholar 

  33. Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)

    Article  Google Scholar 

  34. Zhao, W., et al.: Automated brain MRI volumetry differentiates early stages of Alzheimer’s disease from normal aging. J. Geriatr. Psychiatry Neurol. 32(6), 354–364 (2019)

    Article  Google Scholar 

  35. Schwarz, C.G., et al.: A large-scale comparison of cortical thickness and volume methods for measuring Alzheimer’s disease severity. NeuroImage: Clin. 11, 802–812 (2016)

    Google Scholar 

  36. Voevodskaya, O., Simmons, A., Nordenskjöld, R., Kullberg, J., Ahlström, H., Lind, L., Wahlund, L.O., Larsson, E.M., Westman, E. and Alzheimer’s Disease Neuroimaging Initiative: The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease. Front. Aging Neurosci. 6, 264 (2014)

    Google Scholar 

  37. Trojacanec, K., Kitanovski, I., Dimitrovski, I., Loshkovska, S.: The influence of quality control on the image retrieval: application to longitudinal images for Alzheimer’s disease. In: Proceedings of the 14th International Conference for Informatics and Information Technology, pp. 37–42 (2017)

    Google Scholar 

  38. Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437–1447 (2003)

    Article  Google Scholar 

  39. Toledo, J.B., et al.: Relationship between plasma analytes and SPARE-AD defined brain atrophy patterns in ADNI. PLoS ONE 8(2), e55531 (2013)

    Article  Google Scholar 

  40. Davatzikos, C., Xu, F., An, Y., Fan, Y., Resnick, S.M.: Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 132(8), 2026–2035 (2009)

    Article  Google Scholar 

  41. Habes, M., et al.: White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain 139(4), 1164–1179 (2016)

    Article  Google Scholar 

Download references

Acknowledgement

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovik 16, PO Box 393, Skopje, Macedonia

    Katarina Trojachanec Dineva, Ivan Kitanovski, Ivica Dimitrovski & Suzana Loshkovska

Authors
  1. Katarina Trojachanec Dineva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ivan Kitanovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ivica Dimitrovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Suzana Loshkovska
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Katarina Trojachanec Dineva .

Editor information

Editors and Affiliations

  1. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Katerina Zdravkova

  2. Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Lasko Basnarkov

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dineva, K.T., Kitanovski, I., Dimitrovski, I., Loshkovska, S., for the Alzheimer’s Disease Neuroimaging Initiative. (2022). Combining Static and Dynamic Features to Improve Longitudinal Image Retrieval for Alzheimer’s Disease. In: Zdravkova, K., Basnarkov, L. (eds) ICT Innovations 2022. Reshaping the Future Towards a New Normal. ICT Innovations 2022. Communications in Computer and Information Science, vol 1740. Springer, Cham. https://doi.org/10.1007/978-3-031-22792-9_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-22792-9_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22791-2

  • Online ISBN: 978-3-031-22792-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation