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Nonnegative Matrix Factorization Methods for Brain Tumor Segmentation in Magnetic Resonance Images

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Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 121))

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Abstract

Automatic segmentation of objects in magnetic resonance images (MRI) is very challenging to provide reliable and automated computerized methods for clinical usage because this is a time-consuming process which requires more and more attention. MR imaging is the most usable technique of images to segment the tumor region from brain. By follow the novel framework of nonnegative matrix factorization (NNMF) along with fuzzy clustering and region growing, authors are able to do the tumor segmentation easily and accurately in this research work. By applying these methods, this paper represents the successful extraction of the whole tumor along with narcotic and edema from brain MR, T2, and FLAIR images. This proposed methodology is tested on the BRATS 2012 database with low-grade glioma (LGG) and high-grade glioma (HGG) brain tumor images. Moreover, along with NNMF, fuzzy c-means clustering and region growing, the algorithms of Dice, sensitivity, specificity, and Hausdorff are also evaluated to check the accuracy of the results.

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References

  1. Joseph, R.P., Senthil Singh, C., Manikandan, M.: Brain tumor MRI image segmentation and detection in image processing. IJRTE 3, 1–5 (2014)

    Google Scholar 

  2. Kadkhodaei, M., Samavi, S., Karimi, N., Mohaghegh, H., Soroushmmehr, S.M.R.: Automatic segmentation of multimodal brain tumor images based on classification of super-voxel. IEEE (2016)

    Google Scholar 

  3. Remma Mathew, A., Antop, B.: Tumor detection and classification of MRI brain image using wavelet transform and SVM. ICSPC 4, 75–78 (2017)

    Google Scholar 

  4. Goceri, E., Songul, C.: Automated detection and extraction of skull from MR head images: preliminary results. IEEE (2017)

    Google Scholar 

  5. Li, Q., Gao, Z.: Glioma segmentation using a novel unified algorithm in multimodal MRI images. IEEE Access 1–9 (2018)

    Google Scholar 

  6. Anwar, S.M., et al.: Brain tumor segmentation on multi modal MRI scan using EMAP algorithm. IEEE (2018)

    Google Scholar 

  7. Tamilselvan, K.S., Murugesan, G., Gnanasekaran, B.: Brain tumor detection from clinical CT and MRI images using WT-FCM algorithm. IEEE (2013)

    Google Scholar 

  8. Veer, S.S., Patil, P.M.: An efficient method for segmentation and detection of brain tumor in MRI images. IRJET 2, 912–916 (2015)

    Google Scholar 

  9. Sara, S., Yassine, S.T., Achraf, B., Ahmed, H.: New method of tumor extraction using a histogram study. IEEE (2015)

    Google Scholar 

  10. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural network in MRI image. IEEE Trans. Med. Imaging 35 (2016)

    Google Scholar 

  11. Kapoor, D., Kashyup, R.: segmentation of brain tumor from MRI using skull stripping and neural network. IJEDR 4, 593–598 (2016)

    Google Scholar 

  12. Pezoulas, V.C., Pologiorgi, I., Seferlis, S., Giakos, G.C.: Tissue classification approach for brain tumor using MRI. IEEE (2017)

    Google Scholar 

  13. Nerurkar, S.N.: Brain tumor detection using image segmentation. IJERCSE 4, 65–70 (2017)

    Google Scholar 

  14. Avachar, V., Mushrif, M., Dubey, Y.: Implementation of brain MRI image segmentation algorithm on DSP. IEEE, pp. 2066–2070 (2017)

    Google Scholar 

  15. Milletari, F., Ahmad, S., Kroll, C., Plate, A.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound 92–102 (2017)

    Google Scholar 

  16. Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., Heng, P.A.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 40–54 (2017)

    Google Scholar 

  17. Baid, U.: Novel approach foe brain tumor segmentation with non negative matrix factorization. IEEE, pp. 101–105 (2017)

    Google Scholar 

  18. Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: Voxresnet: deep voxelwise residual networks for brain segmentation from DMR images. NeuroImage 446–455 (2018)

    Google Scholar 

  19. Pardeep Kumar Reddy, R., Nagaraju, C.: Brain tumor MRI using gradient profile sharpness. IJANA 9, 3557–3562 (2018)

    Google Scholar 

  20. Dogra, J., Prashar, N., Jain, S., Sood, M.: Improved methods for analyzing MRI brain images. IAEES 8, 1–11 (2018)

    Google Scholar 

  21. Somasundaram, K., Helen Mercina, J., Magesh, S., Kalaiselvi, T.: Brain portion extraction scheme using region growing and morphological operation from MRI of human head scans. IJCSE 6, 298–302 (2018)

    Google Scholar 

  22. Dolz, J., Desrosiers, C., Ayed, I.B.: 3D fully convolutional networks for sub cortical segmentation in MRI: a large-scale study. NeuroImage 456–470 (2018)

    Google Scholar 

  23. Harshavardhan, A., Babu, S., Venugopal, T.: An improved brain tumor segmentation method from MRI brain images

    Google Scholar 

  24. Ezhilarasan, K., Somasundaram, K., Kalaiselvi, T.: A simple method for automatic brain extraction from T1-W magnetic resonance images (MRI) of human head scans. IJCSE 6 (2018)

    Google Scholar 

  25. Guo, L., Chen, L., Philip Chen, C.L., Zhou, J.: Integrating guided filter into fuzzy clustering for noise image segmentation. Digit. Signal Process. 235–248 (2018)

    Google Scholar 

  26. Zeinalkhani, L., Alijamaat, A., Rostami, K.: Diagnosis of brain tumor using combination of k means clustering and genetic algorithms. IJMI (2018)

    Google Scholar 

  27. Ge, C., Gu, I.Y.H., Jakola, A.S., Yang, J.: Deep learning and multi-sensor for glioma classification using multi stream 2D convolutional networks. IEEE (2018)

    Google Scholar 

  28. Vinoth, R., Venkatesh, C.: Segmentation and detection of tumor in MRI images using CNN and SVM classification. IEEE (2018)

    Google Scholar 

  29. Dobe, O., Sarkar, A., Halder, A.: Rough K-means and morphological operation-based brain tumor extraction. Springer, Berlin (2019)

    Google Scholar 

  30. Nitta, G.R., Sravani, T., Nitta, S., Muthu, B.A.: Dominant gray level based K-means algorithm for MRI images. Health Technol. (2019)

    Google Scholar 

  31. Yang, A., Yang, X., Wu, W., Liu, H., Zhuansun, Y.: Research on feature extraction of tumor image based on convolutional neural network. IEEE Access (2019)

    Google Scholar 

  32. Jemimma, T.A., Jacob Vetharaj, Y.: Watershed algorithm based DAPP feature for brain tumor segmentation and classification. IEEE (2018)

    Google Scholar 

  33. Kumar, M., Sinha, A., Bansode, N.: Detection of brain tumor in MRI images by applying segmentation and area calculus method using SCILAB. IEEE (2018)

    Google Scholar 

  34. Ma, C., et al.: Concatenated and connected random forests with multiscale patch driven contour model for automated brain tumor segmentation of MR images. Trans. Med. Imaging (2018)

    Google Scholar 

  35. Nasiri, N., et al.: A controlled generative model for segmentation of liver tumors. ICEE (2019)

    Google Scholar 

  36. Lakshmi Narayan, T., et al.: An efficient optimization techniques to detect brain tumor from MRI images. ICSSIT (2018)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Punjabi University Patiala, Patiala, Punjab, 147002, India

    Harinder Kaur & Ram Singh

Authors
  1. Harinder Kaur
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  2. Ram Singh
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Corresponding author

Correspondence to Harinder Kaur .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India

    Pradeep Kumar Singh

  2. Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Gdańsk, Poland

    Wiesław Pawłowski

  3. Department of Computer Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India

    Sudeep Tanwar

  4. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Neeraj Kumar

  5. The National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí, Brazil

    Joel J. P. C. Rodrigues

  6. King Abdullah II School of Information Technology, University of Jordan, Amman, Jordan

    Mohammad Salameh Obaidat

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Kaur, H., Singh, R. (2020). Nonnegative Matrix Factorization Methods for Brain Tumor Segmentation in Magnetic Resonance Images. In: Singh, P., Pawłowski, W., Tanwar, S., Kumar, N., Rodrigues, J., Obaidat, M. (eds) Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Lecture Notes in Networks and Systems, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-15-3369-3_28

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