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Biomedical Engineering Letters

, Volume 8, Issue 1, pp 5–28 | Cite as

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

  • Sajid Iqbal
  • M. Usman Ghani Khan
  • Tanzila Saba
  • Amjad Rehman
Review Article

Abstract

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.

Keywords

Human brain cancer diagnosis and analysis Magnetic resonance imaging Human brain tumor multi-classification 

Notes

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of Engineering and TechnologyLahorePakistan
  2. 2.College of Computer and Information SciencesPrince Sultan UniversityRiyadhSaudi Arabia
  3. 3.College of Computer and Information SystemsAl-Yamamah UniversityRiyadhSaudi Arabia

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