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MRI Images Segmentation for Alzheimer Detection Using Multi-agent Systems

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) (AI2SD 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 914))

Abstract

Neurodegenerative diseases such as Alzheimer’s disease (AD), present increasing challenges. Determining the sequence and evolution of the symptoms and pathologies of AD will enable pre-symptom differential diagnosis, and treatment monitoring. Current diagnosis of Alzheimer is made by clinical, neuropsychological, and neuroimaging assessments. In fact, Magnetic Resonance Imaging (MRI) can be considered as the best neuroimaging examination for AD due to the well-defined measurement of brain structures, especially the size of the hippocampus and related regions. Image processing techniques has been used for processing the (MRI) image. Multi-agent Systems (MAS) is a strong paradigm full of complexity that offers promoters solution. We present a MAS solution that aims to automate the search and optimization of image processing. In this survey we propose a three-dimensional (3D) segmentation process based on cooperative MAS.

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References

  1. Citron, M.: Alzheimer’s disease: strategies for disease modification. Nat. Rev. Drug Discov. 9, 387–398 (2010)

    Article  Google Scholar 

  2. Dong, S., Duan, Y., Hu, Y., Zhao, Z.: Advances in the pathogenesis of Alzheimer’ s disease: a re-evaluation of amyloid cascade hypothesis. Transl. Neurodegener. 1, 18 (2012)

    Article  Google Scholar 

  3. Varghese, T., Sheelakumari, R., James, J.S., Mathuranath, P.: A review of neuroimaging biomarkers of Alzheimer’s disease. Neurol. Asia 18(3), 239–248 (2013)

    Google Scholar 

  4. Knight, M.J., McCann, B., Kauppinen, R.A., Coulthard, E.J.: Magnetic resonance imaging to detect early molecular and cellular changes in Alzheimer’s disease. Front. Aging Neurosci. 8, 139 (2016)

    Article  Google Scholar 

  5. Khan, A.M., Ravi, S.: Image segmentation methods: a comparative study. Int. J. Soft Comput. Eng. (IJSCE) 3(4) (2013). ISSN : 2231-2307

    Google Scholar 

  6. Khare, P., Mittal, N.: Analysis of medical image segmentation techniques. Int. Bull. Math. Res. 2, 188–193 (2015)

    Google Scholar 

  7. Newton, R.D.: The identity of Alzheimer’s disease and sinile dementia and their relationship to senility. J. Ment. Sci. 94(395), 225–249 (1948)

    Article  Google Scholar 

  8. Coyle, J.T., Price, D.L., DeLong, M.R.: Alzheimer’s disease: a disorder of cortical cholinergic innervation. Science 219(4589), 1184–1190 (1983)

    Article  Google Scholar 

  9. Wong, C.W., Quaranta, V., Glenner, G.G.: Neuritic plaques and cerebrovascular amyloid in Alzheimer disease are antigenically related. Proc. Natl. Acad. Sci. U.S.A. 82(24), 8729–8732 (1985)

    Article  Google Scholar 

  10. Nukina, N., Ihara, Y.: One of the antigenic determinants of paired helical filaments is related to tau protein. J. Biochem. 99(5), 1541–1544 (1986)

    Article  Google Scholar 

  11. de la Torre, J.C., Mussivand, T.: Can disturbed brain microcirculation cause Alzheimer’s disease. Neurol. Res. 15(3), 146–153 (1993)

    Article  Google Scholar 

  12. Contestabile, A.: The history of the cholinergic hypothesis. Behav. Brain Res. 221(2), 334–340 (2011)

    Article  Google Scholar 

  13. Cochran, J.N., Hall, A.M., Roberson, E.D.: The dendritic hypothesis for Alzheimer’s disease pathophysiology. Brain Res. Bull. 103, 18–28 (2013)

    Article  Google Scholar 

  14. Barnes, D.E., Yaffe, K.: The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 10, 819–828 (2011)

    Article  Google Scholar 

  15. Camicioli, R.: Distinguishing different dementias. Can. Rev. Alzheimer’s Dis. Other Dement. 9, 4–11 (2006)

    Google Scholar 

  16. Paulus, M.P.: The role of neuroimaging for the diagnosis and treatment of anxiety disorders. Depress Anxiety 25(4), 348–356 (2008)

    Article  Google Scholar 

  17. Freidl, W., Schmidt, R., Stronegger, W.J., Reinhart, B.: The impact of sociodemographic, environmental, and behavioural factors, and cerebrovascular risk factors as potential predictors of the mattis dementia rating scale. J. Gerontol. 52(2), 111–116 (1997)

    Article  Google Scholar 

  18. Small, G.W., La Rue, A., Komo, S., Kaplan, A., Mandelkern, M.A.: Predictors of cognitive change in middle-aged and older adults with memory loss. Am. J. Psychiatry 152(12), 1757–1764 (1995)

    Article  Google Scholar 

  19. Nyberg, L., Backman, L., Erngrund, K., Olofsson, U., Nilsson, L.G.: Age differences in episodic memory, semantic memory, and priming: relationships to demographic, intellectual, and biological factors. J. Gerontol. 51(4), 234–240 (1996)

    Article  Google Scholar 

  20. Morris, R.G.: Recent developments in the neuropsychology of dementia. Int. Rev. Psychiatry 6, 85–107 (1994)

    Article  Google Scholar 

  21. Baddeley, A.D.: Working memory. Science 255, 556–559 (1992)

    Article  Google Scholar 

  22. Mitrushina, M., Uchiyama, C., Satz, P.: Heterogeneity of cognitive profiles in normal aging: implications for early manifestations of Alzheimer’s disease. J. Clin. Exp. Neuropsychol. 17(3), 374–382 (1995)

    Article  Google Scholar 

  23. Satel, S., Lilienfeld, S.O.: Brainwashed: The Seductive Appeal of Mindless Neuroscience. Basic Books, New York City (2015). ISBN 978-0465062911

    Google Scholar 

  24. Machairas, V., Baldeweck, T., Walter, T., Decencière, E.: New general features based on superpixels for image segmentation learning, February 2016. https://hal.archives-ouvertes.fr/hal-01276132

  25. Shapiro, L.G., Haralick, R.M.: Computer and Robot Vision, vol. 1. Addison-Wesley, Boston (1992)

    Google Scholar 

  26. Chardin, A.: Modèles énergétiques hièrarchiques pour la résolution des problèmes inverses en analyse d’images, France (2000)

    Google Scholar 

  27. Ferber, J.: Les Systèmes Multi-agents: vers une Intelligence Collective. InterEditions, Paris (1995)

    MATH  Google Scholar 

  28. Mazouzi, S., Guessoum, Z., Michel, F., Batouche, M.: An agent-based approach, for range image segmentation. In: Jamali, N., Scerri, P., Sugawara, T. (eds.) Massively Multi-agent Technology. LNAI, vol. 5043, pp. 146–161. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  29. Spinu, C., Garbay, C., Chassery, J.: A multi-agent approach to edge detection as a distributed optimization problem. In: Proceedings of the 13th International Conference on Pattern Recognition, Austria, vol. 2, pp. 81–85 (1996)

    Google Scholar 

  30. Boucher, A.: A society of goal-oriented agents for the analysis of living cells. Artif. Intell. Med. 14(1–2), 183–196 (1998)

    Article  Google Scholar 

  31. Germond, L.: A cooperative framework for segmentation of MRI brain scan. Artif. Intell. Med. 20(1), 77–93 (2000)

    Article  Google Scholar 

  32. Duchesnay, E.: An agent-based implementation of irregular pyramid for distributed image segmentation. In: Proceedings of the 8th International Conference on Emerging Technologies and Factory, France, pp. 409–415 (2001)

    Google Scholar 

  33. Richard, N., Dojat, M., Garbay, C.: Automated segmentation of human brain MR images using a multi-agent approach. Artif. Intell. Med. 30(2), 75–153 (2004)

    Article  Google Scholar 

  34. Benamrane, N., Nassane, S.: Medical image segmentation by a multi-agent system approach. In: Proceedings of the 5th German Conference on Multiagent System Technologies, Germany, pp. 49–60 (2007)

    Google Scholar 

  35. Chitsaz, M., Seng, W.C.: Medical image segmentation using a multi-agent system approach. Int. Arab J. Inf. Technol. 10(3), 222–229 (2013)

    Google Scholar 

  36. Arsene, O., Dumitrache, I., Mihu, I.: Expert system for medicine diagnosis using software agents. Expert Syst. Appl. 42, 1825–1834 (2015)

    Article  Google Scholar 

  37. Nachour, A., Ouzizi, L., Aoara, Y.: Multi-agent segmentation using region growing and contour detection: syntetic evaluation in MR images with 3D CAD reconstruction. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 8, 115–124 (2016)

    Google Scholar 

  38. Sauwen, N., et al.: Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization. BMC Med. Imaging BMC Ser. – Open Incl Trusted 17, 29 (2017)

    Article  Google Scholar 

  39. Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A.A., Burke, E.K.: A multi-agent based cooperative approach to scheduling and routing. Eur. J. Oper. Res. 254, 169–178 (2016)

    Article  MathSciNet  Google Scholar 

  40. Singh, M., Duggirala, M., Hayatnagarkar, H., Balaraman, V.: A multi-agent model of workgroup behaviour in an enterprise using a compositional approach (2016)

    Google Scholar 

  41. Sadgal, M., El Faziki, A.: Une architecture mult-agents Basée Sur la Catégorisation dans le Traitement d’images. In: International Conference on Image an Signal Processing, Agadir, 3–5 May 2001, pp. 608–617 (2001)

    Google Scholar 

  42. McKhann, G.M., et al.: The diagnosis of dementia due to 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), 263–269 (2011)

    Article  Google Scholar 

  43. Shadlen, M., Larson, E.: Evaluation of cognitive impairment and dementia. In: Basow, D. (ed.) Up To Date, Waltham (2010)

    Google Scholar 

  44. Dickson, D.W.: Neuropathology of Alzheimer’s disease and other dementias’. Clin. Geriatr. Med. 17(2), 209–228 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Laboratory of Genetics, Neuroendocrinology and Biotechnology, Faculty of Sciences, Ibn Tofail University, BP 133, Kenitra, Morocco

    Kenza Arbai

  2. Computer Science Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Bd Prince My Abdellah, BP 2390, Marrakech, Morocco

    Hanane Allioui

Authors
  1. Kenza Arbai
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  2. Hanane Allioui
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Corresponding author

Correspondence to Kenza Arbai .

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Editors and Affiliations

  1. Computer Sciences Department, Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaâdi University, Souani Tangier, Morocco

    Mostafa Ezziyyani

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Arbai, K., Allioui, H. (2019). MRI Images Segmentation for Alzheimer Detection Using Multi-agent Systems. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-11884-6_27

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