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Evaluation of Local Thresholding Algorithms for Segmentation of White Matter Hyperintensities in Magnetic Resonance Images of the Brain

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Applied Informatics (ICAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1455))

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Abstract

White matter hyperintensities are distinguished in magnetic resonance images as areas of abnormal signal intensity. In clinical research, determining the region and position of these hyperintensities in brain MRIs is critical; it is believed this will find applications in clinical practice and will support the diagnosis, prognosis, and therapy monitoring of neurodegenerative diseases. The properties of hyperintensities vary greatly, thus segmenting them is a challenging task. A substantial amount of time and effort has gone into developing satisfactory automatic segmentation systems.

In this work, a wide range of local thresholding algorithms has been evaluated for the segmentation of white matter hyperintensities. Nine local thresholding approaches implemented in ImageJ software are considered: Bernsen, Contrast, Mean, Median, MidGrey, Niblack, Otsu, Phansalkar, Sauvola. Additionally, the use of other local algorithms (Local Normalization and Statistical Dominance Algorithm) with global thresholding was evaluated. The segmentation accuracy results for all algorithms, and the parameter spaces of the best algorithms are presented.

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References

  1. Anbeek, P., Vincken, K.L., Van Osch, M.J., Bisschops, R.H., Van Der Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 21(3), 1037–1044 (2004)

    Article  Google Scholar 

  2. Balakrishnan, R., Hernández, M.d.C.V., Farrall, A.J.: Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data-a systematic review. Computerized Medical Imaging and Graphics, p. 101867 (2021)

    Google Scholar 

  3. Basak, H., Rana, A.: F-UNet: a modified U-Net architecture for segmentation of stroke lesion. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds.) CVIP 2020. CCIS, vol. 1376, pp. 32–43. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1086-8_4

  4. Bernsen, J.: Dynamic thresholding of gray-level images. In: Proceedings Eighth International Conference on Pattern Recognition, Paris, 1986 (1986)

    Google Scholar 

  5. Brickman, A.M., Sneed, J.R., Provenzano, F.A., Garcon, E., Johnert, L., Muraskin, J., Yeung, L.K., Zimmerman, M.E., Roose, S.P.: Quantitative approaches for assessment of white matter hyperintensities in elderly populations. Psychiatry Res. Neuroimaging 193(2), 101–106 (2011)

    Article  Google Scholar 

  6. Caligiuri, M., Perrotta, P., Augimeri, A., Rocca, F., Quattrone, A., Cherubini, A.: Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review. Neuroinformatics 13, 261–276 (2015)

    Article  Google Scholar 

  7. De Boer, R., Vrooman, H.A., Van Der Lijn, F., Vernooij, M.W., Ikram, M.A., Van Der Lugt, A., Breteler, M.M., Niessen, W.J.: White matter lesion extension to automatic brain tissue segmentation on MRI. Neuroimage 45(4), 1151–1161 (2009)

    Article  Google Scholar 

  8. DeCarli, C., et al.: Measures of brain morphology and infarction in the framingham heart study: establishing what is normal. Neurobiol. Aging 26(4), 491–510 (2005)

    Google Scholar 

  9. Frey, B.M., Petersen, M., Mayer, C., Schulz, M., Cheng, B., Thomalla, G.: Characterization of white matter hyperintensities in large-scale MRI-studies. Front. Neurol. 10, 238 (2019)

    Article  Google Scholar 

  10. Kim, K.W., MacFall, J.R., Payne, M.E.: Classification of white matter lesions on magnetic resonance imaging in elderly persons. Biol. Psychiatry 64(4), 273–280 (2008). https://doi.org/10.1016/j.biopsych.2008.03.024. Stress and Synaptic Plasticity

  11. Krig, S.: Computer Vision Metrics: Survey, Taxonomy, Analysis. Apress Open (2014). https://doi.org/10.1007/978-1-4302-5930-5

  12. Liu, L., Chen, S., Zhu, X., Zhao, X.M., Wu, F.X., Wang, J.: Deep convolutional neural network for accurate segmentation and quantification of white matter hyperintensities. Neurocomputing 384, 231–242 (2020)

    Article  Google Scholar 

  13. Maillard, P., Delcroix, N., Crivello, F., Dufouil, C., Gicquel, S., Joliot, M., Tzourio-Mazoyer, N., Alpérovitch, A., Tzourio, C., Mazoyer, B.: An automated procedure for the assessment of white matter hyperintensities by multispectral (t1, t2, pd) MRI and an evaluation of its between-centre reproducibility based on two large community databases. Neuroradiology 50(1), 31–42 (2008)

    Article  Google Scholar 

  14. Milewska, K., Obuchowicz, R., Piorkowski, A.: A preliminary approach to plaque detection in MRI brain images. In: Innovations and Developments of Technologies in Medicine, Biology amd Healthcare - Proceedings of the IEEE EMB International Student Conference 2020. AISC. Springer (2022)

    Google Scholar 

  15. Mutterer, J., Rasband, W.: Imagej macro language programmers reference guide v1. 46d. RSB Homepage, pp. 1–45 (2012)

    Google Scholar 

  16. Niblack, W.: An Introduction to Digital Image Processing, 115–116 Prentice Hall. Englewood Cliffs, New Jersey (1986)

    Google Scholar 

  17. Nichele, L., Persichetti, V., Lucidi, M., Cincotti, G.: Quantitative evaluation of imagej thresholding algorithms for microbial cell counting. OSA Continuum 3(6), 1417–1427 (2020). https://doi.org/10.1364/OSAC.393971

    Article  Google Scholar 

  18. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  19. Park, G., Hong, J., Duffy, B.A., Lee, J.M., Kim, H.: White matter hyperintensities segmentation using the ensemble u-net with multi-scale highlighting foregrounds. Neuroimage 237, 118140 (2021)

    Google Scholar 

  20. Phansalkar, N., More, S., Sabale, A., Joshi, M.: Adaptive local thresholding for detection of nuclei in diversity stained cytology images. In: 2011 International Conference on Communications and Signal Processing, pp. 218–220. IEEE (2011)

    Google Scholar 

  21. Piórkowski, A.: A statistical dominance algorithm for edge detection and segmentation of medical images. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technologies in Medicine. AISC, vol. 471, pp. 3–14. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39796-2_1

    Chapter  Google Scholar 

  22. Sage, D., Unser, M.: Easy Java programming for teaching image-processing. In: Proceedings of 2001 International Conference on Image Processing. vol. 3, pp. 298–301. IEEE (2001)

    Google Scholar 

  23. Sauvola, J., Pietikäinen, M.: Adaptive document image binarization. Pattern Recogn. 33(2), 225–236 (2000)

    Article  Google Scholar 

  24. Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675 (2012)

    Article  Google Scholar 

  25. Soille, P.: Morphological Image Analysis. Springer (2004)

    Google Scholar 

  26. Sundaresan, V., et al.: Automated lesion segmentation with bianca: Impact of population-level features, classification algorithm and locally adaptive thresholding. NeuroImage 202, 116056 (2019). https://doi.org/10.1016/j.neuroimage.2019.116056

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Acknowledgement

This publication was funded by AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, KBIB no 16.16.120.773.

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Authors and Affiliations

  1. Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30 Av., 30–059, Krakow, Poland

    Adam Piórkowski & Julia Lasek

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  1. Adam Piórkowski
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  2. Julia Lasek
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Corresponding author

Correspondence to Adam Piórkowski .

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Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogotá, Colombia

    Hector Florez

  2. Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Buenos Aires, Argentina

    Ma Florencia Pollo-Cattaneo

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Piórkowski, A., Lasek, J. (2021). Evaluation of Local Thresholding Algorithms for Segmentation of White Matter Hyperintensities in Magnetic Resonance Images of the Brain. In: Florez, H., Pollo-Cattaneo, M.F. (eds) Applied Informatics. ICAI 2021. Communications in Computer and Information Science, vol 1455. Springer, Cham. https://doi.org/10.1007/978-3-030-89654-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-89654-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89653-9

  • Online ISBN: 978-3-030-89654-6

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