A Survey of Brain Tumor Segmentation and Classification Algorithms

A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classifica...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of imaging Jg. 7; H. 9; S. 179
Hauptverfasser: Biratu, Erena Siyoum, Schwenker, Friedhelm, Ayano, Yehualashet Megersa, Debelee, Taye Girma
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 06.09.2021
MDPI
Schlagworte:
Algorithms
Biopsy
Brain
Brain cancer
brain tumor
Breast cancer
Classification
Deep learning
Edema
Feature extraction
Image segmentation
Machine learning
Magnetic fields
Magnetic resonance imaging
Medical imaging
Performance evaluation
Post-processing
region growing
segmentation
shallow machine learning
Tomography
Tumors
ISSN:2313-433X, 2313-433X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models’ performance evaluation metrics.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging7090179