Skip to main content
SpringerLink
Log in

A Modified FCM-Based Brain Lesion Segmentation Scheme for Medical Images

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1024))

Abstract

Segmentation of brain lesion from medical images is a critical problem in the present day. In this work, we have proposed a new distance metric for fuzzy clustering based classification of different brain regions via acquiring accurate lesion structures. The modified distance metric segments the images into different regions by calculating the distances between the cluster centers and object elements, and subsequently classify them via fuzzy clustering. The proposed method can effectively remove noise from the images, which results in a better homogeneous classification of the image. Our method can also accurately segment stroke lesion where the results are near to the ground truth of the stroke lesion. The performance of our method is evaluated on both magnetic resonance images (MRI) and computed tomography (CT) images of brain. The obtained results indicate that our method performs better than the standard fuzzy c-means (FCM), spatial FCM (SFCM), kernelized FCM methods (KFCM), and adaptively regularized kernel-based FCM (ARKFCM) schemes.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Kase, C.S.: Intracerebral hemorrhage: non-hypertensive causes. Stroke 17(4), 590–595 (1986)

    Article  Google Scholar 

  2. Stroke. https://emedicine.medscape.com/article/338385-overview. Last accessed 18 Jan 2017

  3. Kidwell, C.S., Chalela, J.A., Saver, J.L., Starkman, S., Hill, M.D., Demchuk, A.M., Butman, J.A., Patronas, N., Alger, J.R., Latour, L.L., Luby, M.L.: Comparison of MRI and CT for detection of acute intracerebral hemorrhage. JAMA 292(15), 1823–1830 (2004)

    Article  Google Scholar 

  4. Loncaric, S., Dhawan, A.P., Broderick, J., Brott, T.: 3-D image analysis of intra-cerebral brain hemorrhage from digitized CT films. Comput. Methods Programs Biomed. 46(3), 207–216 (1995)

    Article  Google Scholar 

  5. Cosic, D., Loucaric, S.: Computer system for quantitative: analysis of ICH from CT head images. In: Proceedings of the 19th Annual International Conference Engineering in Medicine and Biology Society, vol. 2, pp. 553–556. IEEE (1997)

    Google Scholar 

  6. Maksimovic, R., Stankovic, S., Milovanovic, D.: Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models ‘snakes’. Int. J. Med. Inform. 58, 29–37 (2000)

    Article  Google Scholar 

  7. Hu, Q., Qian, G., Aziz, A., Nowinski, W.L.: Segmentation of brain from computed tomography head images. In: Proceedings of the 27th Annual International Conference Engineering in Medicine and Biology Society, pp. 3375–3378. IEEE (2005)

    Google Scholar 

  8. Yoon, D.Y., Choi, C.S., Kim, K.H., Cho, B.M.: Multidetector-row CT angiography of cerebral vasospasm after aneurysmal subarachnoid hemorrhage: comparison of volume-rendered images and digital subtraction angiography. Am. J. Neuroradiol. 27(2), 370–377 (2006)

    Google Scholar 

  9. Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput. Med. Imaging Graph. 31(4–5), 285–298 (2007)

    Article  Google Scholar 

  10. Bardera, A., Boada, I., Feixas, M., Remollo, S., Blasco, G., Silva, Y., Pedraza, S.: Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput. Med. Imaging Graph. 33(4), 304–311 (2009)

    Article  Google Scholar 

  11. Bhadauria, H.S., Singh, A., Dewal, M.L.: An integrated method for hemorrhage segmentation from brain CT imaging. Comput. Electr. Eng. 39(5), 1527–1536 (2013)

    Article  Google Scholar 

  12. Phillips II, W.E., Velthuizen, R.P., Phuphanich, S., Hall, L.O., Clarke, L.P., Silbiger, M.L.: Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magn. Reson. Imaging 13(2), 277–290 (1995)

    Article  Google Scholar 

  13. Lance, G.N., Williams, W.T.: Mixed-data classificatory programs I—agglomerative systems. Aust. Comput. J. 1(1), 15–20 (1967)

    Google Scholar 

  14. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)

    Article  Google Scholar 

  15. Pham, D.L.: Spatial models for fuzzy clustering. Comput. Vis. Image Underst. 84(2), 285–297 (2001)

    Article  MATH  Google Scholar 

  16. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21(3), 193–199 (2002)

    Article  Google Scholar 

  17. Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 34(4), 1907-1916 (2004)

    Article  Google Scholar 

  18. Zhang, D.Q., Chen, S.C.: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32(1), 37–50 (2004)

    Article  Google Scholar 

  19. Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006)

    Article  Google Scholar 

  20. Wang, J., Kong, J., Lu, Y., Qi, M., Zhang, B.: A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput. Med. Imaging Graph. 32(8), 685–698 (2008)

    Article  Google Scholar 

  21. Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit. 40(3), 825–838 (2007)

    Article  MATH  Google Scholar 

  22. Elazab, A., Wang, C., Jia, F., Wu, J., Li, G., Hu, Q.: Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy-means clustering. Comput. Math. Methods Med. (2015)

    Google Scholar 

  23. Kannan, S.R., Devi, R., Ramathilagam, S., Hong, T.P., Ravikumar, A.: Effective kernel FCM: finding appropriate structure in cancer database. Int. J. Biomath. 9(02), 1650018 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  24. Kannan, S.R., Devi, R., Ramathilagam, S., Hong, T.P.: Effective fuzzy possibilistic c-means: an analyzing cancer medical database. Soft Comput 21(11), 2835–2845 (2017)

    Article  Google Scholar 

  25. Farahani, F.V., Ahmadi, A., Zarandi, M.H.F.: Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Math. Comput. Simul. 149, 48–68 (2018)

    Article  MathSciNet  Google Scholar 

  26. IXI Dataset. https://www.nitrc.org/ir/data/projects/ixi, last accessed 2017/7/8

  27. Kang, S.J.: Multi-user identification-based eye-tracking algorithm using position estimation. Sensors 17(1), 41 (2016)

    Article  Google Scholar 

  28. Zhuang, A.H., Valentino, D.J., Toga, A.W.: Skull-stripping magnetic resonance brain images using a model-based level set. NeuroImage 32(1), 79–92 (2006)

    Article  Google Scholar 

  29. Maier, O., Schröder, C., Forkert, N.D., Martinetz, T., Handels, H.: Classifiers for ischemic stroke lesion segmentation: a comparison study. PloS ONE 10(12), e0145118 (2015)

    Article  Google Scholar 

  30. Dubuisson, M.P., Jain, A.K.: A modified Hausdorff distance for object matching. In: Proceedings of the 12th International Conference on Pattern Recognition, pp. 566–568. IEEE (1994)

    Google Scholar 

Download references

Acknowledgements

We thank Institute Human Ethics Committee (IHEC) of Indian Institute of Technology Roorkee, India for allowing us to collect the CT image dataset of hemorrhagic stroke with its ground truth information from Himalayan Institute of Medical Sciences (HIMS), Dehradun, Uttarakhand, India. The consent to obtain CT scan images of patients has already been taken by the radiologists of HIMS. We also thank Dr. Shailendra Raghuwanshi, Head of Radiology Department, HIMS for providing us his useful suggestions.

Author information

Authors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India

    Anjali Gautam, Debanjan Sadhya & Balasubramanian Raman

Authors
  1. Anjali Gautam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debanjan Sadhya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Balasubramanian Raman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anjali Gautam .

Editor information

Editors and Affiliations

  1. Techno India University, Kolkata, India

    Bidyut B. Chaudhuri

  2. Division of Advanced Information Technology and Computer Science, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gautam, A., Sadhya, D., Raman, B. (2020). A Modified FCM-Based Brain Lesion Segmentation Scheme for Medical Images. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9291-8_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9290-1

  • Online ISBN: 978-981-32-9291-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation