access icon free Automatic method for white matter lesion segmentation based on T1-fluid-attenuated inversion recovery images

The authors propose a fast and effective solution for automatic segmentation of white matter lesions by using T1 and fluid-attenuated inversion recovery (FLAIR) image modalities with no need for manual segmentation and atlas registration. Initially, a brain tissue segmentation method is used to segment the T1 image into cerebrospinal fluid (CSF), grey matter and white matter. Based on the obtained tissue segmentation results, the region of interest (ROI) of the FLAIR image is created by subtracting the CSF from the FLAIR image. Subsequently, the authors calculate the z-score of the intensities in the ROI and define a threshold to perform a preliminary identification of abnormalities from normal tissues. The abnormalities obtained at this stage are used as the prior knowledge for the modified level-set technique. The proposed level set method here is applied based on local Gaussian distribution to precisely detect the boundaries of the white matter lesions in the ROI. The level set method based on local Gaussian distribution fitting energy is robust to the intensity inhomogeneity of MR data and therefore capable of precisely extracting the boundaries of white matter lesions. Experimental analysis and quantitative comparisons with the peak-seeking and state-of-the-art white matter lesion segmentation (WMLS) techniques demonstrate that the algorithm is a stable and effective approach which significantly outperforms other trusted solutions for white matter lesion segmentation.

Inspec keywords: set theory; image segmentation; biomedical MRI; Gaussian distribution; medical image processing; brain; biological tissues

Other keywords: white matter lesion boundary; MR data intensity inhomogeneity; T1-fluid-attenuated inversion recovery image modality; local Gaussian distribution fitting energy; grey matter; brain tissue segmentation method; cerebrospinal fluid; modified level-set technique; automatic white matter lesion segmentation

Subjects: Other topics in statistics; Probability theory, stochastic processes, and statistics; Biomedical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation; Algebra, set theory, and graph theory; Medical magnetic resonance imaging and spectroscopy; Biology and medical computing; Combinatorial mathematics; Optical, image and video signal processing; Combinatorial mathematics; Computer vision and image processing techniques; Other topics in statistics

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 28. Geremia, E., Menze, B.H., Clatz, O., et al: ‘Spatial decision forests for MS lesion segmentation in multi-channel MR images’, MICCAI 2010, Bejing, China, 2010, vol. 13, No. Pt 1, pp. 111118.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 17. Bricq, S., Collet, Ch, Armspach, J.P.: ‘Monrovian segmentation of 3D brain MRI to detect multiple sclerosis lesions’. IEEE Int. Conf. on Image processing, ICIP'2008, San Diego, USA, 2008, pp. 733736.
    10. 10)
    11. 11)
      • 32. Osher, S.: ‘Geometric level set methods in imaging, vision and graphics’ (Springer, 2003), pp. 320.
    12. 12)
    13. 13)
      • 22. Sajja, B.R., Datta, S., He, R., et al: ‘A unified approach for lesion segmentation on MRI of multiple sclerosis’. 26th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society IEMBS'04, 2004, vol. 1, pp. 17781781.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 6. Longstreth, W.T.Jr., Manolio, T.A., Arnold, A., et al.: ‘Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people’, Cardiovasc. Health Study Stroke, 1996, 27, pp. 12741282.
    18. 18)
    19. 19)
      • 36. Punga, M., Gaurav, R., Moraru, L.: ‘Level set method coupled with energy image features for brain MR image segmentation’, Biomed. Eng./Biomedizinische Technik, 2014, 59, (3), 219229.
    20. 20)
      • 13. Xie, Y., Tao, X.: ‘White matter lesion segmentation using machine learning and weakly labeled MR images’, SPIE Med. Imaging: Int. Soc. Opt. Photonics, 2011, 7962, pp. 79622G-1797622G-9.
    21. 21)
      • 42. Altman, E.I., et al: ‘Predicting financial distress of companies: revisiting the z-score and ZETA models’, Stern School of Business, New York University, 2000, pp. 912.
    22. 22)
      • 26. Liu, J., Smith, C.D., Chebrolu, H.: ‘Automatic multiple sclerosis detection based on integrated square estimation’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops, Miami, USA, February 2009, pp. 3138.
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
    40. 40)
    41. 41)
    42. 42)
      • 30. Dugas-Phocion, G., Gonzalez, M.A., Lebrun, C., et al.: ‘Hierarchical segmentation of multiple sclerosis lesions in multi sequence MRI’. IEEE Int. Symp. on biomedical imaging: nano to macro, 2004, vol. 1, pp. 157160.
    43. 43)
    44. 44)
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