A novel white blood cell segmentation scheme based on feature space clustering
Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. Fi...
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Gespeichert in:
| Autor*in: |
Jiang, Kan [verfasserIn] Liao, Qing-Min [verfasserIn] Xiong, Yuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 10(2005), 1 vom: 08. Apr., Seite 12-19 |
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| Übergeordnetes Werk: |
volume:10 ; year:2005 ; number:1 ; day:08 ; month:04 ; pages:12-19 |
| Links: |
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| DOI / URN: |
10.1007/s00500-005-0458-z |
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| 520 | |a Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. | ||
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| 700 | 1 | |a Xiong, Yuan |e verfasserin |4 aut | |
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10.1007/s00500-005-0458-z doi (DE-627)SPR006471536 (SPR)s00500-005-0458-z-e DE-627 ger DE-627 rakwb eng Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation (dpeaa)DE-He213 Feature space clustering (dpeaa)DE-He213 Scale-space filtering (dpeaa)DE-He213 Watershed (dpeaa)DE-He213 Liao, Qing-Min verfasserin aut Xiong, Yuan verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)SPR006469531 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://dx.doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)SPR006471536 (SPR)s00500-005-0458-z-e DE-627 ger DE-627 rakwb eng Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation (dpeaa)DE-He213 Feature space clustering (dpeaa)DE-He213 Scale-space filtering (dpeaa)DE-He213 Watershed (dpeaa)DE-He213 Liao, Qing-Min verfasserin aut Xiong, Yuan verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)SPR006469531 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://dx.doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)SPR006471536 (SPR)s00500-005-0458-z-e DE-627 ger DE-627 rakwb eng Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation (dpeaa)DE-He213 Feature space clustering (dpeaa)DE-He213 Scale-space filtering (dpeaa)DE-He213 Watershed (dpeaa)DE-He213 Liao, Qing-Min verfasserin aut Xiong, Yuan verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)SPR006469531 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://dx.doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)SPR006471536 (SPR)s00500-005-0458-z-e DE-627 ger DE-627 rakwb eng Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation (dpeaa)DE-He213 Feature space clustering (dpeaa)DE-He213 Scale-space filtering (dpeaa)DE-He213 Watershed (dpeaa)DE-He213 Liao, Qing-Min verfasserin aut Xiong, Yuan verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)SPR006469531 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://dx.doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)SPR006471536 (SPR)s00500-005-0458-z-e DE-627 ger DE-627 rakwb eng Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation (dpeaa)DE-He213 Feature space clustering (dpeaa)DE-He213 Scale-space filtering (dpeaa)DE-He213 Watershed (dpeaa)DE-He213 Liao, Qing-Min verfasserin aut Xiong, Yuan verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)SPR006469531 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://dx.doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 10 2005 1 08 04 12-19 |
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Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. |
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Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. |
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Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. |
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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">SPR006471536</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002710.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2005 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-005-0458-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006471536</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-005-0458-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Kan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A novel white blood cell segmentation scheme based on feature space clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">WBC segmentation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature space clustering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Scale-space filtering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Watershed</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liao, Qing-Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xiong, Yuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">10(2005), 1 vom: 08. Apr., Seite 12-19</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2005</subfield><subfield code="g">number:1</subfield><subfield code="g">day:08</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:12-19</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-005-0458-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2005</subfield><subfield code="e">1</subfield><subfield code="b">08</subfield><subfield code="c">04</subfield><subfield code="h">12-19</subfield></datafield></record></collection>
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