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Tumor delineation from 3-D MR brain images

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Abstract

In cancer research, automatic brain tumor detection from 3-D magnetic resonance (MR) images is an important pre-requisite. In this regard, the paper presents a new method for segmentation of brain tumor from MR volumes corrupted with different imaging artifact, such as bias field and noise. It addresses the problems of uncertainty and bias field artifact of brain MR images by means of a segmentation algorithm, termed as CoLoRS (Coherent Local Intensity Rough Segmentation). However, the lack of knowledge about the tumor intensity and the textural properties around tumor surface makes the exclusion or inclusion of tumor or healthy tissues, respectively, in the tumor region obtained by CoLoRS. Therefore, a post-processing technique is introduced for precise delineation of tumor region from healthy brain tissues. It mingles the benefits of morphological operations and theory of rough sets into the region growing approach to improve the result of tumor detection. Several publicly available MR brain tumor data sets, namely, BRATS 12, BRATS 14 and BRATS 19, are used to demonstrate the effectiveness of the proposed method with respect to existing approaches. For real high-grade and low-grade data sets of BRATS-12-14, the Dice coefficient of the proposed algorithm is 0.783443 and 0.787045, respectively, and for BRATS-19, 0.788849 and 0.808582, respectively.

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Data availability

The links for the BRATS 2012, BRATS 2014, and the BRATS 2019 datasets are provided in the manuscript.

References

  1. Ahmed, M.N., Yamany, S.M., Mohamed, N., et al.: 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 

  2. Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRRarXiv:1811.02629 (2018)

  3. Bakas, S., Akbari, H., Sotiras, A., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)

    Article  Google Scholar 

  4. Bal, A., Banerjee, M., Chakrabarti, A., et al.: MRI brain tumor segmentation and analysis using rough-fuzzy c-means and shape based properties. J. King Saud Univ. Comput. Inf. Sci. 34(2), 115–133 (2022)

    Google Scholar 

  5. Banerjee, A., Maji, P.: Rough sets and stomped normal distribution for simultaneous segmentation and bias field correction in brain MR images. IEEE Trans. Image Process. 24(12), 5764–5776 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  6. 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 

  7. Chong, V.F., Zhou, J.Y., Khoo, J.B., et al.: Tongue carcinoma: tumor volume measurement. Int. J. Radiat. Oncol. Biol. Phys. 59(1), 59–66 (2004)

    Article  Google Scholar 

  8. Clark, M.C., Hall, L.O., Goldgof, D.B., et al.: Automatic tumor segmentation using knowledge-based techniques. IEEE Trans. Med. Imaging 117, 187–201 (1998)

    Article  Google Scholar 

  9. Farnoosh, R., Noushkaran, H.: Application of a modified combinational approach to brain tumor detection in MR images. J. Digit. Imaging 35, 1421–1432 (2022)

    Article  Google Scholar 

  10. Gong, M., Liang, Y., Shi, J., et al.: Fuzzy C-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process. 22(2), 573–584 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  11. Gunasekara, S.R., Kaldera, H.N.T.K., Dissanayake, M.B.: A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. J. Healthc. Eng. 2021, 1–13 (2021)

    Article  Google Scholar 

  12. Hsieh, T.M., Liu, Y.M., Liao, C.C., et al.: Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing. BMC Med. Inform. Decis Making 11(54), 1–12 (2011)

    Google Scholar 

  13. Iscan, Z., Dokur, Z., Ölmez, T.: Tumor detection by using Zernike moments on segmented magnetic resonance brain images. Expert Syst. Appl. 37(3), 2540–2549 (2010)

    Article  Google Scholar 

  14. Kannan, S.: A new segmentation system for brain MR images based on fuzzy techniques. Appl. Soft Comput. 8(4), 1599–1606 (2008)

    Article  Google Scholar 

  15. Kole, D.K., Halder, A.: Automatic brain tumor detection and isolation of tumor cells from MRI images. Int. J. Comput. Appl. 39(16), 26–30 (2012)

    Google Scholar 

  16. Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  17. Li, C., Gore, J.C., Davatzikos, C.: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn. Reson. Imaging 32(7), 913–923 (2014)

    Article  Google Scholar 

  18. Maji, P., Roy, S.: SoBT-RFW: rough-fuzzy computing and wavelet analysis based automatic brain tumor detection method from MR image. Fund. Inform. 142, 237–267 (2015)

    MathSciNet  MATH  Google Scholar 

  19. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)

    Article  Google Scholar 

  20. Porz, N., Bauer, S., Pica, A., et al.: Multi-modal glioblastoma segmentation: man versus machine. PLoS ONE 9(5), e96873 (2014)

    Article  Google Scholar 

  21. Rexilius, J., Hahn, H.K., Klein, J., et al.: Multispectral brain tumor segmentation based on histogram model adaptation. In: Proceedings of SPIE Conference on Medical Imaging: Computer-Aided Diagnosis, pp. 1–10 (2007)

  22. Roy, S., Maji, P.: A new post-processing method to detect brain tumor using rough-fuzzy clustering. In: Pattern Recognition and Machine Intelligence, pp. 407–417. Springer, Berlin (2015)

    Chapter  Google Scholar 

  23. Roy, S., Maji, P.: Rough segmentation of coherent local intensity for bias induced 3-D MR brain images. Pattern Recogn. 97, 1–18 (2020). https://doi.org/10.1016/j.patcog.2019.106997

    Article  Google Scholar 

  24. Salman, Y.M.: Modified technique for volumetric brain tumor measurements. J. Biomed. Sci. Eng. 2, 16–19 (2009)

  25. Shaik, N.S., Cherukuri, T.K.: Multi-level attention network: application to brain tumor classification. SIViP 16, 817–824 (2022)

    Article  Google Scholar 

  26. Sobhaninia, Z., Rezaei, S., Karimi, N., et al.: Brain tumor segmentation by cascaded deep neural networks using multiple image scales. In: 2020 28th Iranian Conference on Electrical Engineering (ICEE), pp. 1–4. IEEE (2020)

  27. Tustison, N.J., Avants, B.B., Cook, P.A., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Article  Google Scholar 

  28. Widz, S., Slezak, D.: Granular Attribute Selection: A Case Study of Rough Set Approach to MRI Segmentation, pp. 47–52. Springer, Berlin (2013)

    Google Scholar 

  29. Zhang, L., Lan, C., Fu, L., et al.: Segmentation of brain tumor MRI image based on improved attention module Unet network. Signal Image Video Process. (2023)

  30. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Kolkata, West Bengal, 700015, India

    Shaswati Roy

  2. Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, West Bengal, 700108, India

    Pradipta Maji

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  1. Shaswati Roy
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  2. Pradipta Maji
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SR and PM have made equal contributions to the concept, design, and revision of the paper.

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Correspondence to Shaswati Roy.

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Roy, S., Maji, P. Tumor delineation from 3-D MR brain images. SIViP 17, 3433–3441 (2023). https://doi.org/10.1007/s11760-023-02565-4

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