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Survey on brain tumor segmentation and feature extraction of MR images

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Abstract

Brain tumor analysis plays an important role in medical imaging applications and in delivering a huge amount of anatomical and functional information, which increases and simplifies the diagnosis and disease therapy planning. However, the presence of image artifacts such as noise, intensity inhomogeneity and partial volume effect in magnetic resonance images can aggressively affect the quantitative brain tumor analysis. Also, the complex anatomy of the brain is another necessary factor to deal with. To avoid or reduce manual segmentation error, the automatic segmentation and detection of tumor have become the most challenging task for radiologists and clinicians. In this paper, most commonly used MR brain image segmentation algorithms and most popular brain MRI features are surveyed and summarized with an emphasis on their characteristics, merits, and demerits of these techniques. This paper presents a categorization of various segmentation algorithms ranging from simple threshold methods to high-level segmentation techniques such as deformable methods, graph-based, and deep learning approaches with a focus on gliomas which is most common of all malignant brain and central nervous system tumors. We also discuss the current trends with a focus on brain tumor segmentation, tissue segmentation and lesion detection using deep learning methods such as deep neural networks and convolutional neural networks. We also mentioned the future improvements to standardize the MRI-based brain tumor detection method for clinical use.

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Images are generated by using BRATS 2013 data [7]

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  1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India

    Sangeetha Saman & Swathi Jamjala Narayanan

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  1. Sangeetha Saman
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  2. Swathi Jamjala Narayanan
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Correspondence to Sangeetha Saman.

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Saman, S., Jamjala Narayanan, S. Survey on brain tumor segmentation and feature extraction of MR images. Int J Multimed Info Retr 8, 79–99 (2019). https://doi.org/10.1007/s13735-018-0162-2

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  • DOI: https://doi.org/10.1007/s13735-018-0162-2

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