An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation
• A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, Qing [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Umfang: |
15 |
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Enthalten in: Association between dopa decarboxylase gene variants and borderline personality disorder - Mobascher, Arian ELSEVIER, 2014, the journal of the Pattern Recognition Society, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:82 ; year:2018 ; pages:79-93 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.patcog.2018.05.008 |
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| 520 | |a • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. | ||
| 520 | |a • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. | ||
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10.1016/j.patcog.2018.05.008 doi GBV00000000000264A.pica (DE-627)ELV043491847 (ELSEVIER)S0031-3203(18)30172-9 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cai, Qing verfasserin aut An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. Liu, Huiying oth Zhou, Sanping oth Sun, Jingfeng oth Li, Jing oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:82 year:2018 pages:79-93 extent:15 https://doi.org/10.1016/j.patcog.2018.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 82 2018 79-93 15 045F 000 |
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10.1016/j.patcog.2018.05.008 doi GBV00000000000264A.pica (DE-627)ELV043491847 (ELSEVIER)S0031-3203(18)30172-9 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cai, Qing verfasserin aut An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. Liu, Huiying oth Zhou, Sanping oth Sun, Jingfeng oth Li, Jing oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:82 year:2018 pages:79-93 extent:15 https://doi.org/10.1016/j.patcog.2018.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 82 2018 79-93 15 045F 000 |
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10.1016/j.patcog.2018.05.008 doi GBV00000000000264A.pica (DE-627)ELV043491847 (ELSEVIER)S0031-3203(18)30172-9 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cai, Qing verfasserin aut An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. Liu, Huiying oth Zhou, Sanping oth Sun, Jingfeng oth Li, Jing oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:82 year:2018 pages:79-93 extent:15 https://doi.org/10.1016/j.patcog.2018.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 82 2018 79-93 15 045F 000 |
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10.1016/j.patcog.2018.05.008 doi GBV00000000000264A.pica (DE-627)ELV043491847 (ELSEVIER)S0031-3203(18)30172-9 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cai, Qing verfasserin aut An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation 2018transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. • A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. Liu, Huiying oth Zhou, Sanping oth Sun, Jingfeng oth Li, Jing oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:82 year:2018 pages:79-93 extent:15 https://doi.org/10.1016/j.patcog.2018.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 82 2018 79-93 15 045F 000 |
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An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation |
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• A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. |
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• A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. |
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• A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial. |
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An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV043491847</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626003743.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180726s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.patcog.2018.05.008</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000264A.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV043491847</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0031-3203(18)30172-9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield 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"><subfield code="a">15</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A novel adaptive scale operator is proposed based on image entropy and Gaussian kernel function, which not only realizes the adaptive adjustment of scale for the ASACM according to the different degrees of intensity inhomogeneity but also improves the segmentation speed and robustness to initial contours and to noise. • By distributing a dependent-membership function for each pixel, we construct an improved bias field estimation term to estimate the bias field of severe intensity inhomogeneous image that further improves the segmentation accuracy. • To avoid the time-consuming re-initialization process and to solve the instability in traditional penalty term, we define an improved penalty term using piecewise polynomial.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Huiying</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Sanping</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Jingfeng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jing</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Mobascher, Arian ELSEVIER</subfield><subfield code="t">Association between dopa decarboxylase gene variants and borderline personality disorder</subfield><subfield code="d">2014</subfield><subfield code="d">the journal of the Pattern Recognition Society</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV017326583</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:82</subfield><subfield code="g">year:2018</subfield><subfield code="g">pages:79-93</subfield><subfield code="g">extent:15</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.patcog.2018.05.008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield 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