Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm

Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffnes...
Ausführliche Beschreibung

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Autor*in:	Minwei Xia [verfasserIn] Peng Ao [verfasserIn] Bin Zhang [verfasserIn] Yongjun Liao [verfasserIn] Huixue Zhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: Journal of Sensors - Hindawi Limited, 2008, (2022)
Übergeordnetes Werk:	year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1155/2022/2199262

Katalog-ID:	DOAJ028333624

Internformat


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520			\|a Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy.
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allfields	10.1155/2022/2199262 doi (DE-627)DOAJ028333624 (DE-599)DOAJ57b47dbe643245a3a3b5e7822c73819e DE-627 ger DE-627 rakwb eng T1-995 Minwei Xia verfasserin aut Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy. Technology (General) Peng Ao verfasserin aut Bin Zhang verfasserin aut Yongjun Liao verfasserin aut Huixue Zhao verfasserin aut In Journal of Sensors Hindawi Limited, 2008 (2022) (DE-627)550736751 (DE-600)2397931-8 1687725X nnns year:2022 https://doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/article/57b47dbe643245a3a3b5e7822c73819e kostenfrei http://dx.doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/toc/1687-7268 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
spelling	10.1155/2022/2199262 doi (DE-627)DOAJ028333624 (DE-599)DOAJ57b47dbe643245a3a3b5e7822c73819e DE-627 ger DE-627 rakwb eng T1-995 Minwei Xia verfasserin aut Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy. Technology (General) Peng Ao verfasserin aut Bin Zhang verfasserin aut Yongjun Liao verfasserin aut Huixue Zhao verfasserin aut In Journal of Sensors Hindawi Limited, 2008 (2022) (DE-627)550736751 (DE-600)2397931-8 1687725X nnns year:2022 https://doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/article/57b47dbe643245a3a3b5e7822c73819e kostenfrei http://dx.doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/toc/1687-7268 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfields_unstemmed	10.1155/2022/2199262 doi (DE-627)DOAJ028333624 (DE-599)DOAJ57b47dbe643245a3a3b5e7822c73819e DE-627 ger DE-627 rakwb eng T1-995 Minwei Xia verfasserin aut Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy. Technology (General) Peng Ao verfasserin aut Bin Zhang verfasserin aut Yongjun Liao verfasserin aut Huixue Zhao verfasserin aut In Journal of Sensors Hindawi Limited, 2008 (2022) (DE-627)550736751 (DE-600)2397931-8 1687725X nnns year:2022 https://doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/article/57b47dbe643245a3a3b5e7822c73819e kostenfrei http://dx.doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/toc/1687-7268 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfieldsGer	10.1155/2022/2199262 doi (DE-627)DOAJ028333624 (DE-599)DOAJ57b47dbe643245a3a3b5e7822c73819e DE-627 ger DE-627 rakwb eng T1-995 Minwei Xia verfasserin aut Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy. Technology (General) Peng Ao verfasserin aut Bin Zhang verfasserin aut Yongjun Liao verfasserin aut Huixue Zhao verfasserin aut In Journal of Sensors Hindawi Limited, 2008 (2022) (DE-627)550736751 (DE-600)2397931-8 1687725X nnns year:2022 https://doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/article/57b47dbe643245a3a3b5e7822c73819e kostenfrei http://dx.doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/toc/1687-7268 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
allfieldsSound	10.1155/2022/2199262 doi (DE-627)DOAJ028333624 (DE-599)DOAJ57b47dbe643245a3a3b5e7822c73819e DE-627 ger DE-627 rakwb eng T1-995 Minwei Xia verfasserin aut Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy. Technology (General) Peng Ao verfasserin aut Bin Zhang verfasserin aut Yongjun Liao verfasserin aut Huixue Zhao verfasserin aut In Journal of Sensors Hindawi Limited, 2008 (2022) (DE-627)550736751 (DE-600)2397931-8 1687725X nnns year:2022 https://doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/article/57b47dbe643245a3a3b5e7822c73819e kostenfrei http://dx.doi.org/10.1155/2022/2199262 kostenfrei https://doaj.org/toc/1687-7268 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2022
language	English
source	In Journal of Sensors (2022) year:2022
sourceStr	In Journal of Sensors (2022) year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Technology (General)
isfreeaccess_bool	true
container_title	Journal of Sensors
authorswithroles_txt_mv	Minwei Xia @@aut@@ Peng Ao @@aut@@ Bin Zhang @@aut@@ Yongjun Liao @@aut@@ Huixue Zhao @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	550736751
id	DOAJ028333624
language_de	englisch
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callnumber-first	T - Technology
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topic_title	T1-995 Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm
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title	Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm
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title_full	Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm
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title_sort	automatic diagnosis of elbow arthritis based on edge algorithm
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title_auth	Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm
abstract	Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy.
abstractGer	Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy.
abstract_unstemmed	Osteoarthritis is an age-related degenerative joint disease; it is mainly because the cartilage tissue between bones is worn and thinned, which leads to the damage of the periosteum and bone including the surrounding ligaments. Clinically, its manifestations are mainly joint pain, swelling, stiffness, and even partial loss of function, which seriously affects the quality of life of patients. The main clinical manifestations are elbow joint pain and limited movement. Elbow articular cartilage degenerates and falls off, and the more serious manifestation is subchondral hyperosteogeny and sclerosis, which leads to unsmooth articular surface and narrow joint space. Finally, elbow joint pain is severe with different degrees of mobility disorder, elbow joint extension and flexion range is getting smaller and smaller, and elbow joint pain is getting more and more serious. In this paper, the segmentation of left and right elbow images is completed based on gray projection through the analysis of image gray distribution. After obtaining the region of interest of elbow joint, the extraction algorithm of elbow joint hard bone edge is studied. Firstly, the extraction of elbow joint hard bone contour edge is completed based on active shape model algorithm combined with image characteristics. Finally, according to the extraction results of hard bone contour edge, this paper realizes the automatic diagnosis of multiple elbow arthritis indexes and compares with the results given by the image set, which proves that the whole algorithm has good adaptability and accuracy.
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title_short	Automatic Diagnosis of Elbow Arthritis Based on Edge Algorithm
url	https://doi.org/10.1155/2022/2199262 https://doaj.org/article/57b47dbe643245a3a3b5e7822c73819e http://dx.doi.org/10.1155/2022/2199262 https://doaj.org/toc/1687-7268
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