http://iet.metastore.ingenta.com
1887

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Systems Biology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

References

    1. 1)
      • J. Bishop .
        1. Bishop, J.: ‘Stochastic searching networks’. Proc. First IEE Conf. on Artificial Neural Networks, London, UK, 1989, pp. 329331.
        . Proc. First IEE Conf. on Artificial Neural Networks , 329 - 331
    2. 2)
      • M.M. al-Rifaie , M. Bishop .
        2. al-Rifaie, M.M., Bishop, M.: ‘Stochastic diffusion search review’, Paladyn, J. Behav. Robot., 2013, 4, (3), pp. 155173.
        . Paladyn, J. Behav. Robot. , 3 , 155 - 173
    3. 3)
      • M.M. al Rifaie , A. Aber , R. Sayers .
        3. al Rifaie, M.M., Aber, A., Sayers, R., et al: ‘Deploying swarm intelligence in medical imaging’. 2014 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), 2014, pp. 1421.
        . 2014 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM) , 14 - 21
    4. 4)
      • M.M. al-Rifaie , A. Aber , R. Raisys .
        4. al-Rifaie, M.M., Aber, A., Raisys, R.: ‘Swarming robots and possible medical applications’. Int. Society for the Electronic Arts (ISEA 2011), Istanbul, Turkey, 2011.
        . Int. Society for the Electronic Arts (ISEA 2011)
    5. 5)
      • M.M. al-Rifaie , A. Aber , M. Bishop .
        5. al-Rifaie, M.M., Aber, A., Bishop, M.: ‘Swarms search for cancerous lesions: artificial intelligence use for accurate identification of bone metastasis on bone scans’. The European Federation of National Associations of Orthopaedics and Traumatology (EFORT), 13th EFORT Congress, Berlin, Germany, 2012.
        . The European Federation of National Associations of Orthopaedics and Traumatology (EFORT), 13th EFORT Congress
    6. 6)
      • M.M. al-Rifaie , A. Aber .
        6. al-Rifaie, M.M., Aber, A.: ‘Identifying metastasis in bone scans with stochastic diffusion search’. IEEE Information Technology in Medicine and Education (ITME), 2012. Available at: http://www.dx.doi.org/10.1109/ITiME.2012.6291355.
        . IEEE Information Technology in Medicine and Education (ITME)
    7. 7)
      • M.M. al-Rifaie , A. Aber , A.M. Oudah .
        7. al-Rifaie, M.M., Aber, A., Oudah, A.M.: ‘Utilising stochastic diffusion search to identify metastasis in bone scans and microcalcifications on mammographs’. IEEE Bioinformatics and Biomedicine (BIBM 2012), Multiscale Biomedical Imaging Analysis (MBIA 2012), 2012, pp. 280287. Available at: http://www.dx.doi.org/10.1109/BIBMW.2012.6470317.
        . IEEE Bioinformatics and Biomedicine (BIBM 2012), Multiscale Biomedical Imaging Analysis (MBIA 2012) , 280 - 287
    8. 8)
    9. 9)
      • (2001)
        9. N. C. for Health Statistics: ‘Deaths, percent of total deaths and death rates for the 15 leading causes of death: United States and each state, 2000’ (National Center for Health Statistics, 2001).
        .
    10. 10)
    11. 11)
    12. 12)
      • F. Arko , K. Filis , S. Seidel .
        12. Arko, F., Filis, K., Seidel, S., et al: ‘How many patients with infrarenal aneurysms are candidates for endovascular repair? The Northern California experience’, J. Inf., 2004, 11, (1), pp. 3340.
        . J. Inf. , 1 , 33 - 40
    13. 13)
    14. 14)
    15. 15)
    16. 16)
      • B. Stanley , J. Semmens , Q. Mai .
        16. Stanley, B., Semmens, J., Mai, Q., et al: ‘Evaluation of patient selection guidelines for endoluminal aaa repair with the zenith stent-graft: the Australasian experience’, J. Inf., 2001, 8, (5), pp. 457464.
        . J. Inf. , 5 , 457 - 464
    17. 17)
      • N. Guidelines . (2011)
        17. Guidelines, N.: ‘Reducing the harm caused by misplaced nasogastric feeding tubes in adults, children and infants’ (NHS, 2011).
        .
    18. 18)
      • R. Miller . (1981)
        18. Miller, R.: ‘Simultaneous statistical inference’ (Springer-Verlag Inc., 175 FIFTH AVE., New York, NY, 1981, 300, 1981).
        .
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • Z. Merınský , E. Hoštálková , A. Procházka .
        22. Merınský, Z., Hoštálková, E., Procházka, A.: ‘Brain tumour diagnostic support based on medical image segmentation’, Brain, 2009, 40, (60), p. 80.
        . Brain , 60 , 80
    23. 23)
      • S.J. Hussain , T.S. Savithri , P.S. Devi .
        23. Hussain, S.J., Savithri, T.S., Devi, P.S.: ‘Segmentation of tissues in brain mri images using dynamic neuro-fuzzy technique’, Int. J. Soft Comput. Eng. (IJSCE) ISSN, 2012, 1, (6), pp. 22312307.
        . Int. J. Soft Comput. Eng. (IJSCE) ISSN , 6 , 2231 - 2307
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-syb.2015.0036
Loading

Related content

content/journals/10.1049/iet-syb.2015.0036
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address