Document Type
Article
Source of Publication
Complexity
Publication Date
1-1-2020
Abstract
© 2020 Sujeet More et al. Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.
DOI Link
ISSN
Publisher
Hindawi Limited
Volume
2020
Last Page
13
Disciplines
Computer Sciences | Education
Keywords
Diagnosis, Diseases, Histology, Machine learning, Magnetic resonance imaging, Magnetism, Resonance, Tissue, Acquisition process, Automatic approaches, Knee magnetic resonance images, Machine learning techniques, Performance metrics, Reproducibilities, Rheumatoid arthritis, Segmentation techniques, Image segmentation
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
More, Sujeet; Singla, Jimmy; Abugabah, Ahed; and Alzubi, Ahmad Ali, "Machine Learning Techniques for Quantification of Knee Segmentation from MRI" (2020). All Works. 2295.
https://zuscholars.zu.ac.ae/works/2295
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series