Machine Learning Techniques for Quantification of Knee Segmentation from MRI
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 expl...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6613191 |
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| author | Sujeet More Jimmy Singla Ahed Abugabah Ahmad Ali AlZubi |
| author_facet | Sujeet More Jimmy Singla Ahed Abugabah Ahmad Ali AlZubi |
| author_sort | Sujeet More |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ce49846d1c5a417db3716eaa3ddfb6a2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ce49846d1c5a417db3716eaa3ddfb6a22025-02-03T06:46:30ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66131916613191Machine Learning Techniques for Quantification of Knee Segmentation from MRISujeet More0Jimmy Singla1Ahed Abugabah2Ahmad Ali AlZubi3School of Computer Science and Engineering, Lovely Professional University, Kapurthala, IndiaSchool of Computer Science and Engineering, Lovely Professional University, Kapurthala, IndiaCollege of Technological Innovation, Zayed University, Dubai, UAEComputer Science Department, King Saud University, Riyadh, Saudi ArabiaMagnetic 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.http://dx.doi.org/10.1155/2020/6613191 |
| spellingShingle | Sujeet More Jimmy Singla Ahed Abugabah Ahmad Ali AlZubi Machine Learning Techniques for Quantification of Knee Segmentation from MRI Complexity |
| title | Machine Learning Techniques for Quantification of Knee Segmentation from MRI |
| title_full | Machine Learning Techniques for Quantification of Knee Segmentation from MRI |
| title_fullStr | Machine Learning Techniques for Quantification of Knee Segmentation from MRI |
| title_full_unstemmed | Machine Learning Techniques for Quantification of Knee Segmentation from MRI |
| title_short | Machine Learning Techniques for Quantification of Knee Segmentation from MRI |
| title_sort | machine learning techniques for quantification of knee segmentation from mri |
| url | http://dx.doi.org/10.1155/2020/6613191 |
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