A two-level method for segmenting cytological images based on active contour model
Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active cont...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Murashov, D. M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2008 |
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| Schlagwörter: |
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| Anmerkung: |
© Pleiades Publishing, Ltd. 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Pattern recognition and image analysis - Moscow : MAIK Nauka/Interperiodica Publ., 1996, 18(2008), 1 vom: Jan., Seite 177-192 |
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| Übergeordnetes Werk: |
volume:18 ; year:2008 ; number:1 ; month:01 ; pages:177-192 |
| Links: |
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| DOI / URN: |
10.1134/S1054661808010215 |
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| Katalog-ID: |
SPR020172311 |
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| 245 | 1 | 2 | |a A two-level method for segmenting cytological images based on active contour model |
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| 520 | |a Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. | ||
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10.1134/S1054661808010215 doi (DE-627)SPR020172311 (SPR)S1054661808010215-e DE-627 ger DE-627 rakwb eng Murashov, D. M. verfasserin aut A two-level method for segmenting cytological images based on active contour model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2008 Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. Active Contour (dpeaa)DE-He213 Object Boundary (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Initial Contour (dpeaa)DE-He213 Image Brightness (dpeaa)DE-He213 Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 18(2008), 1 vom: Jan., Seite 177-192 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:18 year:2008 number:1 month:01 pages:177-192 https://dx.doi.org/10.1134/S1054661808010215 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2008 1 01 177-192 |
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10.1134/S1054661808010215 doi (DE-627)SPR020172311 (SPR)S1054661808010215-e DE-627 ger DE-627 rakwb eng Murashov, D. M. verfasserin aut A two-level method for segmenting cytological images based on active contour model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2008 Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. Active Contour (dpeaa)DE-He213 Object Boundary (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Initial Contour (dpeaa)DE-He213 Image Brightness (dpeaa)DE-He213 Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 18(2008), 1 vom: Jan., Seite 177-192 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:18 year:2008 number:1 month:01 pages:177-192 https://dx.doi.org/10.1134/S1054661808010215 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2008 1 01 177-192 |
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10.1134/S1054661808010215 doi (DE-627)SPR020172311 (SPR)S1054661808010215-e DE-627 ger DE-627 rakwb eng Murashov, D. M. verfasserin aut A two-level method for segmenting cytological images based on active contour model 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2008 Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. Active Contour (dpeaa)DE-He213 Object Boundary (dpeaa)DE-He213 Active Contour Model (dpeaa)DE-He213 Initial Contour (dpeaa)DE-He213 Image Brightness (dpeaa)DE-He213 Enthalten in Pattern recognition and image analysis Moscow : MAIK Nauka/Interperiodica Publ., 1996 18(2008), 1 vom: Jan., Seite 177-192 (DE-627)327148179 (DE-600)2044032-7 1555-6212 nnns volume:18 year:2008 number:1 month:01 pages:177-192 https://dx.doi.org/10.1134/S1054661808010215 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2008 1 01 177-192 |
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Murashov, D. M. |
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Murashov, D. M. misc Active Contour misc Object Boundary misc Active Contour Model misc Initial Contour misc Image Brightness A two-level method for segmenting cytological images based on active contour model |
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two-level method for segmenting cytological images based on active contour model |
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A two-level method for segmenting cytological images based on active contour model |
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Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. © Pleiades Publishing, Ltd. 2008 |
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Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. © Pleiades Publishing, Ltd. 2008 |
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Abstract The problem of automatic extraction of cell nuclei in cytological images for automated diagnostics of hematological diseases is considered. A combined two-level segmentation method based on an active contour model is presented. The method employs different strategies to form the active contour model at coarse and fine approximation steps. The capture range of the active contour is extended by employing a wave propagation model. Unsupervised initialization of the active contour involves binarization of the a component of the input image in the CIE Lab color space. The efficiency of the developed method is demonstrated by a computational experiment. © Pleiades Publishing, Ltd. 2008 |
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