Computer-assisted brain tumor type discrimination using magnetic resonance imaging features

Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, ana...

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Bibliographic Details
Published in:Biomedical engineering letters 2018, 8(1), , pp.5-28
Main Authors: Iqbal, Sajid, Khan, M. Usman Ghani, Saba, Tanzila, Rehman, Amjad
Format: Article
Language:English
Subjects:
Biological and Medical Physics
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Brain
Brain cancer
Brain research
Brain tumors
Classification
Digital imaging
Engineering
Image classification
Image processing
Image segmentation
Magnetic resonance imaging
Medical and Radiation Physics
Medical imaging
Neuroimaging
NMR
Nuclear magnetic resonance
Researchers
Review
Review Article
Tumors
의공학
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Summary:Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
ISSN:2093-9868
2093-985X
DOI:10.1007/s13534-017-0050-3