» Articles » PMID: 30603187

Computer-assisted Brain Tumor Type Discrimination Using Magnetic Resonance Imaging Features

Overview
Journal Biomed Eng Lett
Date 2019 Jan 4
PMID 30603187
Citations 17
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

A novel approach for the detection of brain tumor and its classification via independent component analysis.

Gunasundari C, Selva Bhuvaneswari K Sci Rep. 2025; 15(1):8252.

PMID: 40064997 PMC: 11894048. DOI: 10.1038/s41598-025-87934-4.


The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.

Berghout T J Imaging. 2025; 11(1).

PMID: 39852315 PMC: 11766058. DOI: 10.3390/jimaging11010002.


AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings.

Lee J, Lee U, Yoo J, Lee T, Jung J, Kim H Plant Methods. 2024; 20(1):44.

PMID: 38493119 PMC: 10943777. DOI: 10.1186/s13007-024-01171-w.


Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier.

Kifle N, Teti S, Ning B, Donoho D, Katz I, Keating R Bioengineering (Basel). 2023; 10(10).

PMID: 37892919 PMC: 10603997. DOI: 10.3390/bioengineering10101190.


Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers.

Tabari A, Chan S, Omar O, Iqbal S, Gee M, Daye D Cancers (Basel). 2023; 15(1).

PMID: 36612061 PMC: 9817513. DOI: 10.3390/cancers15010063.


References
1.
Upadhyay N, Waldman A . Conventional MRI evaluation of gliomas. Br J Radiol. 2012; 84 Spec No 2:S107-11. PMC: 3473894. DOI: 10.1259/bjr/65711810. View

2.
Watanabe Y, Yamasaki F, Kajiwara Y, Takayasu T, Nosaka R, Akiyama Y . Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. Eur J Radiol. 2013; 82(4):658-63. DOI: 10.1016/j.ejrad.2012.11.037. View

3.
Chung C, Metser U, Menard C . Advances in Magnetic Resonance Imaging and Positron Emission Tomography Imaging for Grading and Molecular Characterization of Glioma. Semin Radiat Oncol. 2015; 25(3):164-71. DOI: 10.1016/j.semradonc.2015.02.002. View

4.
Nayak L, Quant Lee E, Wen P . Epidemiology of brain metastases. Curr Oncol Rep. 2011; 14(1):48-54. DOI: 10.1007/s11912-011-0203-y. View

5.
Lasocki A, Tsui A, Tacey M, Drummond K, Field K, Gaillard F . MRI grading versus histology: predicting survival of World Health Organization grade II-IV astrocytomas. AJNR Am J Neuroradiol. 2014; 36(1):77-83. PMC: 7965922. DOI: 10.3174/ajnr.A4077. View