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Detection of Brain Tumor from MR Image Sequence Using Image Segmentation and Blob’s Centroid

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Smart Trends in Systems, Security and Sustainability

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 18))

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Abstract

This paper proposes a method to search for a probable tumor in magnetic resonance (MR) images of a human brain. Typically, a tumor can be found in some contiguous images of the MR sequence and positions of its appearance in such contiguous images usually have similar centroid thus their corresponding projections should be able to be detected automatically in order to support a user or a doctor for further diagnosis. Once region of a probable tumor is detected, matched checking between a pair of contiguous MR images can be done and relabeled to indicate the same area of the tumor amongst sequential images. Any regions without match between contiguous images are initially considered as irrelevant components and will not be analyzed further unless the doctor indicates otherwise. Then, ratio of tumor to brain is calculated to support as an initial diagnosis of tumors appeared in an MR image sequence .

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References

  1. Evelin, G., Sujji, Y.L.: MRI brain image segmentation based on thresholding. Int. J. Adv. Comput. Res. 2249–7277 (2013)

    Google Scholar 

  2. Mukesh Kumar, K.K.: A texture based tumor detection and automatic segmentation using seed region growing method. IJCTA. 855–859 (2011)

    Google Scholar 

  3. Mashor, Othman, Mat Isa: Seeded region growing features extraction algorithm: its potential use in improving screening for cervical cancer. Int. J. Comput. Int. Manag. 61–70 (2005)

    Google Scholar 

  4. Somasundaram, K., Kalaiselvi, T.: Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations. Comput. Biol. Med. 716–725 (2011)

    Google Scholar 

  5. Nooshin, N., Miroslav, K.: Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comput. Biol. Med. 286–301 (2015)

    Google Scholar 

  6. Anitha, V., Murugavalli, S.: Brain tumor classification using two-tier classifier with adaptive segmentation technique. IET J. 9–18 (2015)

    Google Scholar 

  7. Wei, H., Kab, L.C., Jiayin, Z.: Region-based nasopharyngeal carcinoma lesion segmentation from mri using clustering and classification-based methods with learning. Imag. Informat. Med. 472–482 (2012)

    Google Scholar 

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Acknowledgements

We would like to thank you Maharaj Nakorn Chiang Mai Hospital, Faculty of Medicine, Chiang Mai University, Thailand for example MR image sequences for this research.

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Correspondence to Varin Chouvatut .

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Chaisuparpsirikun, T., Chouvatut, V. (2018). Detection of Brain Tumor from MR Image Sequence Using Image Segmentation and Blob’s Centroid. In: Yang, XS., Nagar, A., Joshi, A. (eds) Smart Trends in Systems, Security and Sustainability. Lecture Notes in Networks and Systems, vol 18. Springer, Singapore. https://doi.org/10.1007/978-981-10-6916-1_11

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  • DOI: https://doi.org/10.1007/978-981-10-6916-1_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6915-4

  • Online ISBN: 978-981-10-6916-1

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