Skip to main content
SpringerLink
Log in
Book cover

International Conference on Image Analysis and Processing

ICIAP 2022: Image Analysis and Processing. ICIAP 2022 Workshops pp 441–449Cite as

Unsupervised Brain Segmentation System Using K-Means and Neural Network

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13373))

Abstract

Voxel-based morphometry is an analysis technique used to quantify matter volume in the human brain from magnetic resonance imaging studies. Atrophies and morphologic changes can be signs of neuronal depletion that can lead to various degenerative diseases. Two of the most worldwide used tools for brain volume assessment and segmentation in white matter, grey matter, and cerebrospinal fluid are the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) and Statistical Parameter Mapping (SPM). However, the main issue of these tools is related to the choice of the best parameter setting.

In this paper, a novel and unsupervised segmentation system, without any user parameter setting, that uses both the k-means clustering algorithm and artificial neural network is proposed. Performance of this system was evaluated on the Internet Brain Segmentation Repository (IBSR) dataset (v.2.0) and results were compared with FSL and SPM results.

The dice similarity score was calculated to compare the segmentations obtained with FSL, SPM, and the proposed system with the reference segmentations. The proposed system resulted fast and reliable, and reduced any error (the dice similarity score was greater than 83%, 86%, and 65% for white matter, grey matter, and cerebrospinal fluid, respectively) demonstrating an improvement in the discrimination among white matter, grey matter, and cerebrospinal fluid.

This is a preview of subscription content, 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   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
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

Learn about institutional subscriptions

References

  1. Dora, L., Agrawal, S., Panda, R., Abraham, A.: State-of-the-art methods for brain tissue segmentation: a review (2017)https://doi.org/10.1109/RBME.2017.2715350

  2. Comelli, A., et al.: Tissue classification to support local active delineation of brain tumors. In: Zheng, Y., Williams, B.M., Chen, K.E. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 3–14. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39343-4_1

    Chapter  Google Scholar 

  3. Staffaroni, A.M., et al.: Neuroimaging in dementia. Semin. Neurol. 37, 510–537 (2017). https://doi.org/10.1055/s-0037-1608808

    Article  Google Scholar 

  4. Alongi, P., et al.: 18F-Florbetaben PET/CT to assess Alzheimer’s disease: a new analysis method for regional amyloid quantification. J. Neuroimaging. 29, 383–393 (2019). https://doi.org/10.1111/jon.12601

    Article  Google Scholar 

  5. Alongi, P., et al.: Radiomics analysis of brain [18F]FDG PET/CT to predict Alzheimer’s disease in patients with amyloid PET positivity: a preliminary report on the application of SPM cortical segmentation. Pyradiom. Mach. Learn. Anal. Diagn. 12, 933 (2022). https://doi.org/10.3390/diagnostics12040933

    Article  Google Scholar 

  6. Griffanti, L., et al.: Classification and characterization of periventricular and deep white matter hyperintensities on MRI: a study in older adults. Neuroimage 170, 174–181 (2018). https://doi.org/10.1016/J.NEUROIMAGE.2017.03.024

    Article  Google Scholar 

  7. Kennedy, D.N., Filipek, P.A., Caviness, V.S.: Anatomic segmentation and volumetric calculations in nuclear. Magn. Reson. Imaging (1989). https://doi.org/10.1109/42.20356

    Article  Google Scholar 

  8. Stefano, A., Gallivanone, F., Messa, C.L., Gilardi, M.C.L., Castiglioni, I.: Metabolic impact of partial volume correction of [18F]FDG PET-CT oncological studies on the assessment of tumor response to treatment. Q. J. Nucl. Med. Mol. Imaging. 58, 413–423 (2014)

    Google Scholar 

  9. Alongi, P., et al.: Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients. Eur. Radiol. 31(7), 4595–4605 (2021). https://doi.org/10.1007/s00330-020-07617-8

    Article  Google Scholar 

  10. Vernuccio, F., Cannella, R., Comelli, A., Salvaggio, G., Lagalla, R., Midiri, M.: Radiomics and artificial intelligence: new frontiers in medicine. Recent. Prog. Med. 111(3), 130–135 (2020). Italian. https://doi.org/10.1701/3315.32853

  11. Comelli, A., et al.: A Kernel support vector machine based technique for Crohn’s disease classification in human patients. In: Barolli, L., Terzo, O. (eds.) CISIS 2017. AISC, vol. 611, pp. 262–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61566-0_25

    Chapter  Google Scholar 

  12. Licari, L., et al.: Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors. G. Chir. 40, 32–38 (2019)

    Google Scholar 

  13. Cuocolo, R., et al.: Clinically significant prostate cancer detection on MRI: a radiomic shape features study. Eur. J. Radiol. 116, 144–149 (2019). https://doi.org/10.1016/j.ejrad.2019.05.006

    Article  Google Scholar 

  14. Tsang, O., Gholipour, A., Kehtarnavaz, N., Gopinath, K., Briggs, R., Panahi, I.: Comparison of tissue segmentation algorithms in neuroimage analysis software tools. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008 - “Personalized Healthcare through Technology.”, pp. 3924–3928 (2008). https://doi.org/10.1109/iembs.2008.4650068

  15. Klauschen, F., Goldman, A., Barra, V., Meyer-Lindenberg, A., Lundervold, A.: Evaluation of automated brain MR image segmentation and volumetry methods. Hum. Brain Mapp. 30, 1310–1327 (2009). https://doi.org/10.1002/hbm.20599

    Article  Google Scholar 

  16. Rajagopalan, V., Pioro, E.P.: Disparate voxel based morphometry (VBM) results between SPM and FSL softwares in ALS patients with frontotemporal dementia: Which VBM results to consider? BMC Neurol. 15, (2015). https://doi.org/10.1186/s12883-015-0274-8

  17. Kazemi, K., Noorizadeh, N.: Quantitative comparison of SPM, FSL, and Brainsuite for Brain MR image segmentation. J. Biomed. Phys. Eng. 4, 13–26 (2014)

    Google Scholar 

  18. Tudorascu, D.L., et al.: Reproducibility and bias in healthy brain segmentation: comparison of two popular neuroimaging platforms. Front. Neurosci. 10 (2016). https://doi.org/10.3389/fnins.2016.00503

  19. Fellhauer, I., et al.: Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer’s dementia or mild cognitive impairment (2015).https://doi.org/10.1016/j.pscychresns.2015.07.011

  20. Stefano, A., et al.: A fully automatic method for biological target volume segmentation of brain metastases. Int. J. Imaging Syst. Technol. 26, 29–37 (2016). https://doi.org/10.1002/ima.22154

    Article  Google Scholar 

  21. Agnello, L., Comelli, A., Ardizzone, E., Vitabile, S.: Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis. Int. J. Imaging Syst. Technol. 26, 136–150 (2016). https://doi.org/10.1002/ima.22168

    Article  Google Scholar 

  22. Comelli, A., Stefano, A.: A fully automated segmentation system of positron emission tomography studies. In: Zheng, Y., Williams, B.M., Ke., Chen (eds.) MIUA 2019. CCIS, vol. 1065, pp. 353–363. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39343-4_30

  23. Woolrich, M.W., et al.: Bayesian analysis of neuroimaging data in FSL. Neuroimage 45, S173–S186 (2009). https://doi.org/10.1016/j.neuroimage.2008.10.055

    Article  Google Scholar 

  24. Friston, K.J.: Statistical Parametric Mapping: The Analysis of Functional Brain Images (2006)

    Google Scholar 

  25. Sharma, G., Martin, J.: MATLAB®: A language for parallel computing. Int. J. Parallel Program. 37, 3–36 (2009). https://doi.org/10.1007/s10766-008-0082-5

    Article  MATH  Google Scholar 

  26. Rohlfing, T.: Image similarity and tissue overlaps as surrogates for image registration accuracy: Widely used but unreliable. IEEE Trans. Med. Imaging. 31, 153–163 (2012). https://doi.org/10.1109/TMI.2011.2163944

    Article  Google Scholar 

  27. Hunter, D., Yu, H., Pukish, M.S., Kolbusz, J., Wilamowski, B.M.: Selection of proper neural network sizes and architectures-A comparative study. IEEE Trans. Ind. Informatics. (2012). https://doi.org/10.1109/TII.2012.2187914

    Article  Google Scholar 

  28. Stefano, A., et al.: A graph-based method for PET image segmentation in radiotherapy planning: a pilot study. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 711–720. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41184-7_72

    Chapter  Google Scholar 

  29. Russo, G., et al.: Feasibility on the use of radiomics features of 11[C]-MET PET/CT in central nervous system tumours: Preliminary results on potential grading discrimination using a machine learning model. Curr. Oncol. 28, 5318–5331 (2021). https://doi.org/10.3390/curroncol28060444

    Article  Google Scholar 

  30. Comelli, A., Stefano, A., Benfante, V., Russo, G.: Normal and abnormal tissue classification in positron emission tomography oncological studies. Pattern Recognit. Image Anal. 28, 106–113 (2018). https://doi.org/10.1134/S1054661818010054

    Article  Google Scholar 

  31. Laudicella, R., Iagaru, A., Minutoli, F., Gaeta, M., Baldari, S., Bisdas, S.: PET/MR in neuro-oncology: is it ready for prime-time? Clin. Transl. Imaging 8(4), 233–235 (2020). https://doi.org/10.1007/s40336-020-00377-x

    Article  Google Scholar 

  32. Cuocolo, R., et al.: Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. J. Magn. Reson. Imaging. 54, 452–459 (2021). https://doi.org/10.1002/jmri.27585

    Article  Google Scholar 

  33. Stefano, A., Comelli, A.: Customized efficient neural network for covid-19 infected region identification in CT images. J. Imaging. 7, 131 (2021). https://doi.org/10.3390/jimaging7080131

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ri.MED Foundation, Via Bandiera 11, 90133, Palermo, Italy

    Riccardo Laudicella & Albert Comelli

  2. Nuclear Medicine Unit, University of Messina, Messina, Italy

    Riccardo Laudicella

  3. Palermo, Italy

    Luca Agnello

Authors
  1. Riccardo Laudicella
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luca Agnello
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Albert Comelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Albert Comelli .

Editor information

Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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

Laudicella, R., Agnello, L., Comelli, A. (2022). Unsupervised Brain Segmentation System Using K-Means and Neural Network. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-13321-3_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13320-6

  • Online ISBN: 978-3-031-13321-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation