Radial-searching contour extraction method based on a modified active contour model for mammographic masses
Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour mode...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Nakagawa, Toshiaki [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: |
© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 |
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| Übergeordnetes Werk: |
Enthalten in: Radiological physics and technology - Tokyo : Springer, 2008, 1(2008), 2 vom: 01. Juli, Seite 151-161 |
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| Übergeordnetes Werk: |
volume:1 ; year:2008 ; number:2 ; day:01 ; month:07 ; pages:151-161 |
| Links: |
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| DOI / URN: |
10.1007/s12194-008-0022-5 |
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| Katalog-ID: |
SPR025175505 |
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| 100 | 1 | |a Nakagawa, Toshiaki |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Radial-searching contour extraction method based on a modified active contour model for mammographic masses |
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| 520 | |a Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. | ||
| 650 | 4 | |a Segmentation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Contour extraction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Active contour model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mammographic mass |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Computer-aided diagnosis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hara, Takeshi |4 aut | |
| 700 | 1 | |a Fujita, Hiroshi |4 aut | |
| 700 | 1 | |a Horita, Katsuhei |4 aut | |
| 700 | 1 | |a Iwase, Takuji |4 aut | |
| 700 | 1 | |a Endo, Tokiko |4 aut | |
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10.1007/s12194-008-0022-5 doi (DE-627)SPR025175505 (SPR)s12194-008-0022-5-e DE-627 ger DE-627 rakwb eng Nakagawa, Toshiaki verfasserin aut Radial-searching contour extraction method based on a modified active contour model for mammographic masses 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. Segmentation (dpeaa)DE-He213 Contour extraction (dpeaa)DE-He213 Active contour model (dpeaa)DE-He213 Mammographic mass (dpeaa)DE-He213 Computer-aided diagnosis (dpeaa)DE-He213 Hara, Takeshi aut Fujita, Hiroshi aut Horita, Katsuhei aut Iwase, Takuji aut Endo, Tokiko aut Enthalten in Radiological physics and technology Tokyo : Springer, 2008 1(2008), 2 vom: 01. Juli, Seite 151-161 (DE-627)571166032 (DE-600)2433581-2 1865-0341 nnns volume:1 year:2008 number:2 day:01 month:07 pages:151-161 https://dx.doi.org/10.1007/s12194-008-0022-5 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 1 2008 2 01 07 151-161 |
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10.1007/s12194-008-0022-5 doi (DE-627)SPR025175505 (SPR)s12194-008-0022-5-e DE-627 ger DE-627 rakwb eng Nakagawa, Toshiaki verfasserin aut Radial-searching contour extraction method based on a modified active contour model for mammographic masses 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. Segmentation (dpeaa)DE-He213 Contour extraction (dpeaa)DE-He213 Active contour model (dpeaa)DE-He213 Mammographic mass (dpeaa)DE-He213 Computer-aided diagnosis (dpeaa)DE-He213 Hara, Takeshi aut Fujita, Hiroshi aut Horita, Katsuhei aut Iwase, Takuji aut Endo, Tokiko aut Enthalten in Radiological physics and technology Tokyo : Springer, 2008 1(2008), 2 vom: 01. Juli, Seite 151-161 (DE-627)571166032 (DE-600)2433581-2 1865-0341 nnns volume:1 year:2008 number:2 day:01 month:07 pages:151-161 https://dx.doi.org/10.1007/s12194-008-0022-5 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 1 2008 2 01 07 151-161 |
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10.1007/s12194-008-0022-5 doi (DE-627)SPR025175505 (SPR)s12194-008-0022-5-e DE-627 ger DE-627 rakwb eng Nakagawa, Toshiaki verfasserin aut Radial-searching contour extraction method based on a modified active contour model for mammographic masses 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. Segmentation (dpeaa)DE-He213 Contour extraction (dpeaa)DE-He213 Active contour model (dpeaa)DE-He213 Mammographic mass (dpeaa)DE-He213 Computer-aided diagnosis (dpeaa)DE-He213 Hara, Takeshi aut Fujita, Hiroshi aut Horita, Katsuhei aut Iwase, Takuji aut Endo, Tokiko aut Enthalten in Radiological physics and technology Tokyo : Springer, 2008 1(2008), 2 vom: 01. Juli, Seite 151-161 (DE-627)571166032 (DE-600)2433581-2 1865-0341 nnns volume:1 year:2008 number:2 day:01 month:07 pages:151-161 https://dx.doi.org/10.1007/s12194-008-0022-5 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 1 2008 2 01 07 151-161 |
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10.1007/s12194-008-0022-5 doi (DE-627)SPR025175505 (SPR)s12194-008-0022-5-e DE-627 ger DE-627 rakwb eng Nakagawa, Toshiaki verfasserin aut Radial-searching contour extraction method based on a modified active contour model for mammographic masses 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. Segmentation (dpeaa)DE-He213 Contour extraction (dpeaa)DE-He213 Active contour model (dpeaa)DE-He213 Mammographic mass (dpeaa)DE-He213 Computer-aided diagnosis (dpeaa)DE-He213 Hara, Takeshi aut Fujita, Hiroshi aut Horita, Katsuhei aut Iwase, Takuji aut Endo, Tokiko aut Enthalten in Radiological physics and technology Tokyo : Springer, 2008 1(2008), 2 vom: 01. Juli, Seite 151-161 (DE-627)571166032 (DE-600)2433581-2 1865-0341 nnns volume:1 year:2008 number:2 day:01 month:07 pages:151-161 https://dx.doi.org/10.1007/s12194-008-0022-5 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 1 2008 2 01 07 151-161 |
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10.1007/s12194-008-0022-5 doi (DE-627)SPR025175505 (SPR)s12194-008-0022-5-e DE-627 ger DE-627 rakwb eng Nakagawa, Toshiaki verfasserin aut Radial-searching contour extraction method based on a modified active contour model for mammographic masses 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. Segmentation (dpeaa)DE-He213 Contour extraction (dpeaa)DE-He213 Active contour model (dpeaa)DE-He213 Mammographic mass (dpeaa)DE-He213 Computer-aided diagnosis (dpeaa)DE-He213 Hara, Takeshi aut Fujita, Hiroshi aut Horita, Katsuhei aut Iwase, Takuji aut Endo, Tokiko aut Enthalten in Radiological physics and technology Tokyo : Springer, 2008 1(2008), 2 vom: 01. Juli, Seite 151-161 (DE-627)571166032 (DE-600)2433581-2 1865-0341 nnns volume:1 year:2008 number:2 day:01 month:07 pages:151-161 https://dx.doi.org/10.1007/s12194-008-0022-5 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 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_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 1 2008 2 01 07 151-161 |
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Enthalten in Radiological physics and technology 1(2008), 2 vom: 01. Juli, Seite 151-161 volume:1 year:2008 number:2 day:01 month:07 pages:151-161 |
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Enthalten in Radiological physics and technology 1(2008), 2 vom: 01. Juli, Seite 151-161 volume:1 year:2008 number:2 day:01 month:07 pages:151-161 |
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Segmentation Contour extraction Active contour model Mammographic mass Computer-aided diagnosis |
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Radiological physics and technology |
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Nakagawa, Toshiaki @@aut@@ Hara, Takeshi @@aut@@ Fujita, Hiroshi @@aut@@ Horita, Katsuhei @@aut@@ Iwase, Takuji @@aut@@ Endo, Tokiko @@aut@@ |
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We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. 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Nakagawa, Toshiaki |
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Nakagawa, Toshiaki misc Segmentation misc Contour extraction misc Active contour model misc Mammographic mass misc Computer-aided diagnosis Radial-searching contour extraction method based on a modified active contour model for mammographic masses |
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Radial-searching contour extraction method based on a modified active contour model for mammographic masses Segmentation (dpeaa)DE-He213 Contour extraction (dpeaa)DE-He213 Active contour model (dpeaa)DE-He213 Mammographic mass (dpeaa)DE-He213 Computer-aided diagnosis (dpeaa)DE-He213 |
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Nakagawa, Toshiaki Hara, Takeshi Fujita, Hiroshi Horita, Katsuhei Iwase, Takuji Endo, Tokiko |
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radial-searching contour extraction method based on a modified active contour model for mammographic masses |
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Radial-searching contour extraction method based on a modified active contour model for mammographic masses |
| abstract |
Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 |
| abstractGer |
Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 |
| abstract_unstemmed |
Abstract In this study, we developed an automatic extraction scheme for the precise recognition of the contours of masses on digital mammograms in order to improve a computer-aided diagnosis (CAD) system. We propose a radial-searching contour extraction method based on a modified active contour model (ACM). In this technique, after determining the central point of a mass by searching for the direction of the density gradient, we arranged an initial contour at the central point, and the movement of a control point was limited to directions radiating from the central point. Moreover, it became possible to increase the extraction accuracy by sorting out the pixel used for processing and using two images—an edge-intensity image and a degree-of-separation image defined based on the pixel-value histogram—for calculation of the image forces used for constraints on deformation of the ACM. We investigated the accuracy of the automated extraction method by using 53 masses with several “difficult contours” on 53 digitized mammograms. The extraction results were compared quantitatively with the “correct segmentation” represented by an experienced physician’s sketches. The numbers of cases in which the extracted region corresponded to the correct region with overlap ratios of more than 81 and 61% were 30 and 45, respectively. The initial results obtained with this technique show that it will be useful for the segmentation of masses in CAD schemes. © Japanese Society of Radiological Technology and Japan Society of Medical Physics 2008 |
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Radial-searching contour extraction method based on a modified active contour model for mammographic masses |
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https://dx.doi.org/10.1007/s12194-008-0022-5 |
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Hara, Takeshi Fujita, Hiroshi Horita, Katsuhei Iwase, Takuji Endo, Tokiko |
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Hara, Takeshi Fujita, Hiroshi Horita, Katsuhei Iwase, Takuji Endo, Tokiko |
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10.1007/s12194-008-0022-5 |
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2024-07-03T14:20:39.935Z |
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| score |
7.3982563 |