Abnormalities detection in wireless capsule endoscopy images using EM algorithm

Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Amiri, Zahra [verfasserIn] Hassanpour, Hamid Beghdadi, Azeddine

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Abnormalities detection Angiodysplasia Capsule endoscopy Ulcer Lymphoid hyperplasia

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 39(2022), 7 vom: 28. Mai, Seite 2999-3010
Übergeordnetes Werk:	volume:39 ; year:2022 ; number:7 ; day:28 ; month:05 ; pages:2999-3010

Links:	Volltext

DOI / URN:	10.1007/s00371-022-02507-0

Katalog-ID:	SPR052091252

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allfields	10.1007/s00371-022-02507-0 doi (DE-627)SPR052091252 (SPR)s00371-022-02507-0-e DE-627 ger DE-627 rakwb eng Amiri, Zahra verfasserin aut Abnormalities detection in wireless capsule endoscopy images using EM algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. Abnormalities detection (dpeaa)DE-He213 Angiodysplasia (dpeaa)DE-He213 Capsule endoscopy (dpeaa)DE-He213 Ulcer (dpeaa)DE-He213 Lymphoid hyperplasia (dpeaa)DE-He213 Hassanpour, Hamid aut Beghdadi, Azeddine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2022), 7 vom: 28. Mai, Seite 2999-3010 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010 https://dx.doi.org/10.1007/s00371-022-02507-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2022 7 28 05 2999-3010
spelling	10.1007/s00371-022-02507-0 doi (DE-627)SPR052091252 (SPR)s00371-022-02507-0-e DE-627 ger DE-627 rakwb eng Amiri, Zahra verfasserin aut Abnormalities detection in wireless capsule endoscopy images using EM algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. Abnormalities detection (dpeaa)DE-He213 Angiodysplasia (dpeaa)DE-He213 Capsule endoscopy (dpeaa)DE-He213 Ulcer (dpeaa)DE-He213 Lymphoid hyperplasia (dpeaa)DE-He213 Hassanpour, Hamid aut Beghdadi, Azeddine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2022), 7 vom: 28. Mai, Seite 2999-3010 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010 https://dx.doi.org/10.1007/s00371-022-02507-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2022 7 28 05 2999-3010
allfields_unstemmed	10.1007/s00371-022-02507-0 doi (DE-627)SPR052091252 (SPR)s00371-022-02507-0-e DE-627 ger DE-627 rakwb eng Amiri, Zahra verfasserin aut Abnormalities detection in wireless capsule endoscopy images using EM algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. Abnormalities detection (dpeaa)DE-He213 Angiodysplasia (dpeaa)DE-He213 Capsule endoscopy (dpeaa)DE-He213 Ulcer (dpeaa)DE-He213 Lymphoid hyperplasia (dpeaa)DE-He213 Hassanpour, Hamid aut Beghdadi, Azeddine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2022), 7 vom: 28. Mai, Seite 2999-3010 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010 https://dx.doi.org/10.1007/s00371-022-02507-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2022 7 28 05 2999-3010
allfieldsGer	10.1007/s00371-022-02507-0 doi (DE-627)SPR052091252 (SPR)s00371-022-02507-0-e DE-627 ger DE-627 rakwb eng Amiri, Zahra verfasserin aut Abnormalities detection in wireless capsule endoscopy images using EM algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. Abnormalities detection (dpeaa)DE-He213 Angiodysplasia (dpeaa)DE-He213 Capsule endoscopy (dpeaa)DE-He213 Ulcer (dpeaa)DE-He213 Lymphoid hyperplasia (dpeaa)DE-He213 Hassanpour, Hamid aut Beghdadi, Azeddine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2022), 7 vom: 28. Mai, Seite 2999-3010 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010 https://dx.doi.org/10.1007/s00371-022-02507-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2022 7 28 05 2999-3010
allfieldsSound	10.1007/s00371-022-02507-0 doi (DE-627)SPR052091252 (SPR)s00371-022-02507-0-e DE-627 ger DE-627 rakwb eng Amiri, Zahra verfasserin aut Abnormalities detection in wireless capsule endoscopy images using EM algorithm 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. Abnormalities detection (dpeaa)DE-He213 Angiodysplasia (dpeaa)DE-He213 Capsule endoscopy (dpeaa)DE-He213 Ulcer (dpeaa)DE-He213 Lymphoid hyperplasia (dpeaa)DE-He213 Hassanpour, Hamid aut Beghdadi, Azeddine aut Enthalten in The visual computer Berlin : Springer, 1985 39(2022), 7 vom: 28. Mai, Seite 2999-3010 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010 https://dx.doi.org/10.1007/s00371-022-02507-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 39 2022 7 28 05 2999-3010
language	English
source	Enthalten in The visual computer 39(2022), 7 vom: 28. Mai, Seite 2999-3010 volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010
sourceStr	Enthalten in The visual computer 39(2022), 7 vom: 28. Mai, Seite 2999-3010 volume:39 year:2022 number:7 day:28 month:05 pages:2999-3010
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Abnormalities detection Angiodysplasia Capsule endoscopy Ulcer Lymphoid hyperplasia
isfreeaccess_bool	false
container_title	The visual computer
authorswithroles_txt_mv	Amiri, Zahra @@aut@@ Hassanpour, Hamid @@aut@@ Beghdadi, Azeddine @@aut@@
publishDateDaySort_date	2022-05-28T00:00:00Z
hierarchy_top_id	254910734
id	SPR052091252
language_de	englisch
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author	Amiri, Zahra
spellingShingle	Amiri, Zahra misc Abnormalities detection misc Angiodysplasia misc Capsule endoscopy misc Ulcer misc Lymphoid hyperplasia Abnormalities detection in wireless capsule endoscopy images using EM algorithm
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topic_title	Abnormalities detection in wireless capsule endoscopy images using EM algorithm Abnormalities detection (dpeaa)DE-He213 Angiodysplasia (dpeaa)DE-He213 Capsule endoscopy (dpeaa)DE-He213 Ulcer (dpeaa)DE-He213 Lymphoid hyperplasia (dpeaa)DE-He213
topic	misc Abnormalities detection misc Angiodysplasia misc Capsule endoscopy misc Ulcer misc Lymphoid hyperplasia
topic_unstemmed	misc Abnormalities detection misc Angiodysplasia misc Capsule endoscopy misc Ulcer misc Lymphoid hyperplasia
topic_browse	misc Abnormalities detection misc Angiodysplasia misc Capsule endoscopy misc Ulcer misc Lymphoid hyperplasia
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title	Abnormalities detection in wireless capsule endoscopy images using EM algorithm
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title_full	Abnormalities detection in wireless capsule endoscopy images using EM algorithm
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author_browse	Amiri, Zahra Hassanpour, Hamid Beghdadi, Azeddine
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doi_str_mv	10.1007/s00371-022-02507-0
title_sort	abnormalities detection in wireless capsule endoscopy images using em algorithm
title_auth	Abnormalities detection in wireless capsule endoscopy images using EM algorithm
abstract	Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstractGer	Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
abstract_unstemmed	Abstract In this paper, a novel method is proposed to detect common abnormalities in Wireless Capsule Endoscopy (WCE) video frames including Lymphoid Hyperplasia, ulcer, and angiodysplasia lesions. Inspecting WCE video frames to detect abnormality is a tedious task for physicians. One important step in the proposed approach is to extract the region of interest (ROI), i.e., suspicious region, using the expectation–maximization (EM) algorithm. Suspicious regions in WCE frames are segmented using the EM algorithm considering the color and texture information of the image. Then, suitable descriptors associated with the shape, texture, and color of ROIs are examined for further analysis. These descriptors include histogram of gradients for shape, local binary patterns for texture and different statistical characteristics from pixel values for color information. These features are then fed to a support-vector machine for classification. The results show that the proposed approach can detect abnormalities in WCE frames with the accuracy of 91.3%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
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container_issue	7
title_short	Abnormalities detection in wireless capsule endoscopy images using EM algorithm
url	https://dx.doi.org/10.1007/s00371-022-02507-0
remote_bool	true
author2	Hassanpour, Hamid Beghdadi, Azeddine
author2Str	Hassanpour, Hamid Beghdadi, Azeddine
ppnlink	254910734
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doi_str	10.1007/s00371-022-02507-0
up_date	2024-07-04T01:13:27.532Z
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Abnormalities detection in wireless capsule endoscopy images using EM algorithm

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