Diagnosis of Alzheimer disease in MR brain images using optimization techniques


Nature-inspired algorithms play a vital role in various applications, namely image processing, engineering, industrialized designs and business. Generally, these algorithms are inspired by the nature which is helpful in segmenting the brain internal regions, namely cerebrospinal fluid, grey matter HC, white matter, ventricle and so on. Segmentation of hippocampus (HC) is a very hectic process due to its anatomical structure of the brain. This work has been recommended for different optimization techniques such as lion optimization algorithm (LOA), genetic algorithm, BAT algorithm, particle swarm optimization and artificial bee colony optimization to segment HC region from the brain subregions. The comparison of these optimization methods has been evaluated, and it showed better performance in LOA due to its individualities of escaping from local optima. From the obtained results, it is witnessed that the LOA has ability to segment HC region with high accuracy of 95%. The LOA method showed the best classification accuracy compared to all other methods. Finally, the mini-mental state examination score validation has been attempted to reach the clinical targets as HC region is a major hallmarks for diagnosing AD. The overall process of the proposed work demonstrates the abnormalities in the brain natural history which provides the reliable and accurate indication to the clinician about AD progression.

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Alzheimer disease


Lion optimization algorithm


Genetic algorithm


Particle swarm optimization


Artificial bee colony optimization


Wind driven optimization


Deep learning


Convolution neural network


Mini-mental score examination


Normal controls


  1. 1.

    Albert MS (1997) The ageing brain: Normal and abnormal memory. Philos Trans R Soc B Biol Sci. https://doi.org/10.1098/rstb.1997.0152

    Article  Google Scholar 

  2. 2.

    Dementia Australia 2002 (2016) [Online] Available: https://www.dementia.org.au/about-dementia/types-ofdementia/alzheimers-disease

  3. 3.

    Brunnström H, Englund E (2011) Comparison of four neuropathological scales for Alzheimer’s disease. Clin Neuropathol. https://doi.org/10.5414/npp30056

    Article  Google Scholar 

  4. 4.

    Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT (2011) Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. https://doi.org/10.1101/cshperspect.a006189

    Article  Google Scholar 

  5. 5.

    Thal DR, Rüb U, Orantes M, Braak H (2002) Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology. https://doi.org/10.1212/WNL.58.12.1791

    Article  Google Scholar 

  6. 6.

    Anand KS, Dhikav V (2017) Hippocampus in health and disease: an overview. Ann Indian Acad Neurol. https://doi.org/10.4103/0972-2327.104323

    Article  Google Scholar 

  7. 7.

    Chakraborty S, Chatterjee S, Dey N, Ashour AS, Ashour AS, Shi F, Mali K (2017) Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech. https://doi.org/10.1002/jemt.22900

    Article  Google Scholar 

  8. 8.

    Mesejo P, Ugolotti R, Cagnoni S, Di Cunto F, Giacobini M (2012) Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest. In: Proceedings—IEEE symposium on computer-based medical systems. https://doi.org/10.1109/CBMS.2012.6266318

  9. 9.

    Talbi H, Batouche M, Draa A (2007) A quantum-inspired evolutionary algorithm for multiobjective image segmentation. Int J Math Phys Eng Sci 1(2):109–114

    Google Scholar 

  10. 10.

    Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41:3538–3560. https://doi.org/10.1016/j.eswa.2013.10.059

    Article  Google Scholar 

  11. 11.

    Hatta NM, Zain AM, Sallehuddin R, Shayfull Z, Yusoff Y (2019) Recent studies on optimization method of Grey Wolf Optimiser (GWO): a review (2014–2017). Artif Intell Rev 52:2651–2683. https://doi.org/10.1007/s10462-018-9634-2

    Article  Google Scholar 

  12. 12.

    Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM (2017) Classification using deep learning neural networks for brain tumors. Futur Comput Inform J. https://doi.org/10.1016/j.fcij.2017.12.001

    Article  Google Scholar 

  13. 13.

    Masters CL, Bateman R, Blennow K, Rowe CC, Sperling RA, Cummings JL (2015) Alzheimer’s disease. Nat Rev Dis Primers 1:15056. https://doi.org/10.1038/nrdp.2015.56

    Article  Google Scholar 

  14. 14.

    Duara R, Loewenstein DA, Potter E, Appel J, Greig MT, Urs R, Potter H (2008) Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology 71(24):1986–1992

    Article  Google Scholar 

  15. 15.

    Duara R, Loewenstein DA, Shen Q, Barker W, Varon D, Greig MT, Potter H (2013) The utility of age-specific cut-offs for visual rating of medial temporal atrophy in classifying Alzheimer’s disease, MCI andcognitively normal elderly subjects. Front Aging Neurosci 5(47):1–8

    Google Scholar 

  16. 16.

    Frisoni GB, Beltramello A, Weiss C, Geroldi C, Bianchetti A, Trabucchi M (1996) Linear measures of atrophy in mild Alzheimer disease. Am J Neuroradiol 17(5):913–923

    Google Scholar 

  17. 17.

    Babalola KO, Patenaude B, Aljabar P, Schnabel J, Kennedy D, Crum W, Smith S, Cootes T, Jenkinson M, Rueckert D (2009) An evaluation of four automatic methods of segmenting the subcortical structures in the brain. Neuroimage. https://doi.org/10.1016/j.neuroimage.2009.05.029

    Article  Google Scholar 

  18. 18.

    Villain N, Desgranges B, Viader F, de la Sayette V, Mezenge F, Landeau B, Baron J-C, Eustache F, Chetelat G (2008) Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer’s disease. J Neurosci. https://doi.org/10.1523/JNEUROSCI.1392-08.2008

    Article  Google Scholar 

  19. 19.

    Wang H, Das SR, Suh JW, Altinay M, Pluta J, Craige C, Yushkevich PA (2011) A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation. NeuroImage 55(3):968–985

    Article  Google Scholar 

  20. 20.

    Chen X, Feng S, Pan D (2015) An improved approach of lung image segmentation based on watershed algorithm. In: Proceedings of the 7th international conference on internet multimedia computing and service

  21. 21.

    Zheng Y, Jeon B, Xu D, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst. https://doi.org/10.3233/IFS-141378

    Article  Google Scholar 

  22. 22.

    Prabha S, Anandh KR, Sujatha CM, Ramakrishnan S (2014) Total variation based edge enhancement for level set segmentation and asymmetry analysis in breast thermograms. In: 2014 36th annual international conference IEEE engineering medicine and biological society EMBC 2014. https://doi.org/10.1109/embc.2014.6945102

  23. 23.

    Kurban T, Civicioglu P, Kurban R, Besdok E (2014) Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2014.05.037

    Article  Google Scholar 

  24. 24.

    Xue Y, Zhong S, Ma T, Cao J (2015) A hybrid evolutionary algorithm for numerical optimization problem. Intell Autom Soft Comput 21(4):473–490. https://doi.org/10.1080/10798587.2014.962239

    Article  Google Scholar 

  25. 25.

    Yang XS, Deb S (2009) Cuckoo search via levy flights. In: 2009 World congress national biologically inspired computing. NABIC 2009—Proc., 2009. https://doi.org/10.1109/nabic.2009.5393690

  26. 26.

    Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.03.032

    Article  Google Scholar 

  27. 27.

    Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng. https://doi.org/10.1016/j.jcde.2015.06.003

    Article  Google Scholar 

  28. 28.

    Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: Proceeding of the international joint conference neural networks. https://doi.org/10.1109/ijcnn.2016.7727519

  29. 29.

    Abdelfattah A (2017) Image classification using deep neural networks—a beginner friendly approach using TensorFlow. https://medium.com/@tifa2up/image-classification-using-deep-neural-networks-a-beginner-friendly-approach-using-tensorflow-94b0a090ccd4

  30. 30.

    Jayasuriya SA, Liew AW-C (2012) Symmetry plane detection in neuroimages based on intensity profile analysis. In: 2012 international symposium on information technology in medicine and education (ITME). IEEE, vol 2

  31. 31.

    Kahramanli H (2012) A modified cuckoo optimization algorithm for engineering optimization. Int J Future Comput Commun 1:199–201. https://doi.org/10.7763/IJFCC.2012.V1.52

    Article  Google Scholar 

  32. 32.

    Alsmadi MK (2015) MRI brain segmentation using a hybrid artificial bee colony algorithm with fuzzy-C mean algorithm. J Appl Sci. https://doi.org/10.3923/jas.2015.100.109

    Article  Google Scholar 

  33. 33.

    Yang XS (2010) A new metaheuristic bat-inspired algorithm. Stud Comput Intell. https://doi.org/10.1007/978-3-642-12538-6_6

    Article  MATH  Google Scholar 

  34. 34.

    Suganthi SS, Ramakrishnan S (2014) Anisotropic diffusion filter based edge enhancement for segmentation of breast thermogram using level sets. Biomed Signal Process Control 10:128–136

    Article  Google Scholar 

  35. 35.

    Vemuri P, Jones DT, Jack CR (2012) Resting state functional MRI in Alzheimer‘s disease. Alzheimer’s Res Ther. https://doi.org/10.1186/alzrt100

    Article  Google Scholar 

  36. 36.

    Cao L, Li L, Zheng J, Fan X, Yin F, Shen H, Zhang J (2018) Multi-task neural networks for joint hippocampus segmentation and clinical score regression. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-5581-1

    Article  Google Scholar 

  37. 37.

    Taherdangkoo M, Bagheri MH, Yazdi M, Andriole KP (2013) An effective method for segmentation of MR brain images using the ant colony optimization algorithm. J Digit Imaging. https://doi.org/10.1007/s10278-013-9596-5

    Article  Google Scholar 

  38. 38.

    Wang X, Li W, Wang X, Qian Z (2010) Segmentation of scalp, skull, CSF, grey matter and white matter in MRI of mouse brain. In: Proceedings of the 2010 3rd international conference biomedical engineering and informatics BMEI 2010. pp 550–554. https://doi.org/10.1109/bmei.2010.5639992

  39. 39.

    Khalifa I, Youssif A, Youssry H (2012) MRI brain image segmentation based on wavelet and FCM algorithm. Int J Comput Appl. https://doi.org/10.5120/7275-0446

    Article  Google Scholar 

  40. 40.

    Sandhya G, Babu Kande G, Savithri TS (2017) Multilevel thresholding method based on electromagnetism for accurate brain MRI segmentation to detect white matter, gray matter, and CSF. Biomed Res Int. https://doi.org/10.1155/2017/6783209

    Article  Google Scholar 

  41. 41.

    Singh C, Bala A (2018) A DCT-based local and non-local fuzzy C-means algorithm for segmentation of brain magnetic resonance images. Appl Soft Comput 68:447–457

    Article  Google Scholar 

  42. 42.

    Pham TX, Siarry P, Oulhadj H (2018) Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2018.01.003

    Article  Google Scholar 

  43. 43.

    Ramaniharan AK, Manoharan SC, Swaminathan R (2016) Laplace Beltrami eigen value based classification of normal and Alzheimer MR images using parametric and non-parametric classifiers. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.04.029

    Article  Google Scholar 

  44. 44.

    Cover GS, Herrera WG, Bento MP, Appenzeller S, Rittner L (2018) Computational methods for corpus callosum segmentation on MRI: a systematic literature review. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2017.10.025

    Article  Google Scholar 

  45. 45.

    Pang S, Jiang J, Lu Z, Li X, Yang W, Huang M, Zhang Y, Feng Y, Huang W, Feng Q (2017) Hippocampus segmentation based on local linear mapping. Sci Rep. https://doi.org/10.1038/srep45501

    Article  Google Scholar 

  46. 46.

    Carmichael OT, Aizenstein HA, Davis SW, Becker JT, Thompson PM, Meltzer CC, Liu Y (2005) Atlas-based hippocampus segmentation in Alzheimer’s disease and mild cognitive impairment. Neuroimage. https://doi.org/10.1016/j.neuroimage.2005.05.005

    Article  Google Scholar 

  47. 47.

    Hao Y, Wang T, Zhang X, Duan Y, Yu C, Jiang T, Fan Y (2014) Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation. Hum Brain Mapp. https://doi.org/10.1002/hbm.22359

    Article  Google Scholar 

  48. 48.

    Kang L, Kumar J, Ye P, Li Y, Doermann D (2014) Convolutional neural networks for document image classification. In: Proceedings of the international conference pattern recognition. https://doi.org/10.1109/icpr.2014.546

  49. 49.

    Xiao Z, Ding Y, Lan T, Zhang C, Luo C, Qin Z (2017) Brain MR image classification for Alzheimer’s disease diagnosis based on multifeature fusion. Comput Math Methods Med. https://doi.org/10.1155/2017/1952373

    Article  Google Scholar 

  50. 50.

    Arunkumar N, Mohammed MA, Abd Ghani MK, Ibrahim DA, Abdulhay E, Ramirez-Gonzalez G, de Albuquerque VHC (2018) K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput. https://doi.org/10.1007/s00500-018-3618-7

    Article  Google Scholar 

  51. 51.

    Chen Y, Pham TD (2013) Development of a brain MRI-based hidden Markov model for dementia recognition. Biomed Eng Online. https://doi.org/10.1186/1475-925X-12-S1-S2

    Article  Google Scholar 

  52. 52.

    Zhang Y, Wang S, Dong Z (2014) Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog Electromagn Res. https://doi.org/10.2528/PIER13121310

    Article  Google Scholar 

  53. 53.

    Zhang Y, Dong Z, Phillips P, Wang S, Ji G, Yang J, Yuan T-F (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigen brain and machine learning. Front Comput Neurosci. https://doi.org/10.3389/fncom.2015.00066

    Article  Google Scholar 

  54. 54.

    Cui R, Liu M (2018) Hippocampus analysis by combination of 3D DenseNet and shapes for Alzheimer’s disease diagnosis. IEEE J Biomed Health Inform 23(5):2099–2107. https://doi.org/10.1109/JBHI.2018.2882392

    Article  Google Scholar 

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The authors are very grateful to Chettinad Hospital for giving the real data to do the study in this particular area. The authors are very glad to work with Dr. Abu Baker, Former HOD, Radiology department, Chettinad Health City, Chennai, for his continuous help in diagnosing AD that significantly improved the quality of the manuscript.

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Chitradevi, D., Prabha, S. & Alex Daniel Prabhu Diagnosis of Alzheimer disease in MR brain images using optimization techniques. Neural Comput & Applic 33, 223–237 (2021). https://doi.org/10.1007/s00521-020-04984-7

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  • Alzheimer disease
  • Clinical dementia rating (CDR)
  • Deep learning
  • Hippocampus
  • MMSE score
  • Optimization technique