A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation
Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Al-Ayyoub, Mahmoud [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media New York 2015 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 71(2015), 8 vom: 23. Apr., Seite 3149-3162 |
|---|---|
| Übergeordnetes Werk: |
volume:71 ; year:2015 ; number:8 ; day:23 ; month:04 ; pages:3149-3162 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11227-015-1431-y |
|---|
| Katalog-ID: |
OLC203394770X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC203394770X | ||
| 003 | DE-627 | ||
| 005 | 20230504053856.0 | ||
| 007 | tu | ||
| 008 | 200819s2015 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11227-015-1431-y |2 doi | |
| 035 | |a (DE-627)OLC203394770X | ||
| 035 | |a (DE-He213)s11227-015-1431-y-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |a 620 |q VZ |
| 100 | 1 | |a Al-Ayyoub, Mahmoud |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media New York 2015 | ||
| 520 | |a Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. | ||
| 650 | 4 | |a Graphics processing unit (GPU) | |
| 650 | 4 | |a Medical imaging | |
| 650 | 4 | |a Fuzzy C-means (FCM) algorithm | |
| 700 | 1 | |a Abu-Dalo, Ansam M. |4 aut | |
| 700 | 1 | |a Jararweh, Yaser |4 aut | |
| 700 | 1 | |a Jarrah, Moath |4 aut | |
| 700 | 1 | |a Sa’d, Mohammad Al |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The journal of supercomputing |d Springer US, 1987 |g 71(2015), 8 vom: 23. Apr., Seite 3149-3162 |w (DE-627)13046466X |w (DE-600)740510-8 |w (DE-576)018667775 |x 0920-8542 |7 nnns |
| 773 | 1 | 8 | |g volume:71 |g year:2015 |g number:8 |g day:23 |g month:04 |g pages:3149-3162 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11227-015-1431-y |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 71 |j 2015 |e 8 |b 23 |c 04 |h 3149-3162 | ||
| author_variant |
m a a maa a m a d ama amad y j yj m j mj m a s ma mas |
|---|---|
| matchkey_str |
article:09208542:2015----::guaeipeettosfhfzymasloihsomd |
| hierarchy_sort_str |
2015 |
| publishDate |
2015 |
| allfields |
10.1007/s11227-015-1431-y doi (DE-627)OLC203394770X (DE-He213)s11227-015-1431-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Al-Ayyoub, Mahmoud verfasserin aut A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm Abu-Dalo, Ansam M. aut Jararweh, Yaser aut Jarrah, Moath aut Sa’d, Mohammad Al aut Enthalten in The journal of supercomputing Springer US, 1987 71(2015), 8 vom: 23. Apr., Seite 3149-3162 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 https://doi.org/10.1007/s11227-015-1431-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 71 2015 8 23 04 3149-3162 |
| spelling |
10.1007/s11227-015-1431-y doi (DE-627)OLC203394770X (DE-He213)s11227-015-1431-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Al-Ayyoub, Mahmoud verfasserin aut A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm Abu-Dalo, Ansam M. aut Jararweh, Yaser aut Jarrah, Moath aut Sa’d, Mohammad Al aut Enthalten in The journal of supercomputing Springer US, 1987 71(2015), 8 vom: 23. Apr., Seite 3149-3162 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 https://doi.org/10.1007/s11227-015-1431-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 71 2015 8 23 04 3149-3162 |
| allfields_unstemmed |
10.1007/s11227-015-1431-y doi (DE-627)OLC203394770X (DE-He213)s11227-015-1431-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Al-Ayyoub, Mahmoud verfasserin aut A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm Abu-Dalo, Ansam M. aut Jararweh, Yaser aut Jarrah, Moath aut Sa’d, Mohammad Al aut Enthalten in The journal of supercomputing Springer US, 1987 71(2015), 8 vom: 23. Apr., Seite 3149-3162 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 https://doi.org/10.1007/s11227-015-1431-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 71 2015 8 23 04 3149-3162 |
| allfieldsGer |
10.1007/s11227-015-1431-y doi (DE-627)OLC203394770X (DE-He213)s11227-015-1431-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Al-Ayyoub, Mahmoud verfasserin aut A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm Abu-Dalo, Ansam M. aut Jararweh, Yaser aut Jarrah, Moath aut Sa’d, Mohammad Al aut Enthalten in The journal of supercomputing Springer US, 1987 71(2015), 8 vom: 23. Apr., Seite 3149-3162 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 https://doi.org/10.1007/s11227-015-1431-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 71 2015 8 23 04 3149-3162 |
| allfieldsSound |
10.1007/s11227-015-1431-y doi (DE-627)OLC203394770X (DE-He213)s11227-015-1431-y-p DE-627 ger DE-627 rakwb eng 004 620 VZ Al-Ayyoub, Mahmoud verfasserin aut A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm Abu-Dalo, Ansam M. aut Jararweh, Yaser aut Jarrah, Moath aut Sa’d, Mohammad Al aut Enthalten in The journal of supercomputing Springer US, 1987 71(2015), 8 vom: 23. Apr., Seite 3149-3162 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 https://doi.org/10.1007/s11227-015-1431-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 71 2015 8 23 04 3149-3162 |
| language |
English |
| source |
Enthalten in The journal of supercomputing 71(2015), 8 vom: 23. Apr., Seite 3149-3162 volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 |
| sourceStr |
Enthalten in The journal of supercomputing 71(2015), 8 vom: 23. Apr., Seite 3149-3162 volume:71 year:2015 number:8 day:23 month:04 pages:3149-3162 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
The journal of supercomputing |
| authorswithroles_txt_mv |
Al-Ayyoub, Mahmoud @@aut@@ Abu-Dalo, Ansam M. @@aut@@ Jararweh, Yaser @@aut@@ Jarrah, Moath @@aut@@ Sa’d, Mohammad Al @@aut@@ |
| publishDateDaySort_date |
2015-04-23T00:00:00Z |
| hierarchy_top_id |
13046466X |
| dewey-sort |
14 |
| id |
OLC203394770X |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC203394770X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504053856.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-015-1431-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC203394770X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11227-015-1431-y-p</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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Al-Ayyoub, Mahmoud</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graphics processing unit (GPU)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medical imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy C-means (FCM) algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abu-Dalo, Ansam M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jararweh, Yaser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jarrah, Moath</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sa’d, Mohammad Al</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The journal of supercomputing</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">71(2015), 8 vom: 23. Apr., Seite 3149-3162</subfield><subfield code="w">(DE-627)13046466X</subfield><subfield code="w">(DE-600)740510-8</subfield><subfield code="w">(DE-576)018667775</subfield><subfield code="x">0920-8542</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:71</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:8</subfield><subfield code="g">day:23</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:3149-3162</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11227-015-1431-y</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">71</subfield><subfield code="j">2015</subfield><subfield code="e">8</subfield><subfield code="b">23</subfield><subfield code="c">04</subfield><subfield code="h">3149-3162</subfield></datafield></record></collection>
|
| author |
Al-Ayyoub, Mahmoud |
| spellingShingle |
Al-Ayyoub, Mahmoud ddc 004 misc Graphics processing unit (GPU) misc Medical imaging misc Fuzzy C-means (FCM) algorithm A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation |
| authorStr |
Al-Ayyoub, Mahmoud |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)13046466X |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science 620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0920-8542 |
| topic_title |
004 620 VZ A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation Graphics processing unit (GPU) Medical imaging Fuzzy C-means (FCM) algorithm |
| topic |
ddc 004 misc Graphics processing unit (GPU) misc Medical imaging misc Fuzzy C-means (FCM) algorithm |
| topic_unstemmed |
ddc 004 misc Graphics processing unit (GPU) misc Medical imaging misc Fuzzy C-means (FCM) algorithm |
| topic_browse |
ddc 004 misc Graphics processing unit (GPU) misc Medical imaging misc Fuzzy C-means (FCM) algorithm |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
The journal of supercomputing |
| hierarchy_parent_id |
13046466X |
| dewey-tens |
000 - Computer science, knowledge & systems 620 - Engineering |
| hierarchy_top_title |
The journal of supercomputing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 |
| title |
A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation |
| ctrlnum |
(DE-627)OLC203394770X (DE-He213)s11227-015-1431-y-p |
| title_full |
A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation |
| author_sort |
Al-Ayyoub, Mahmoud |
| journal |
The journal of supercomputing |
| journalStr |
The journal of supercomputing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
txt |
| container_start_page |
3149 |
| author_browse |
Al-Ayyoub, Mahmoud Abu-Dalo, Ansam M. Jararweh, Yaser Jarrah, Moath Sa’d, Mohammad Al |
| container_volume |
71 |
| class |
004 620 VZ |
| format_se |
Aufsätze |
| author-letter |
Al-Ayyoub, Mahmoud |
| doi_str_mv |
10.1007/s11227-015-1431-y |
| dewey-full |
004 620 |
| title_sort |
a gpu-based implementations of the fuzzy c-means algorithms for medical image segmentation |
| title_auth |
A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation |
| abstract |
Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. © Springer Science+Business Media New York 2015 |
| abstractGer |
Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. © Springer Science+Business Media New York 2015 |
| abstract_unstemmed |
Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM. © Springer Science+Business Media New York 2015 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
8 |
| title_short |
A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation |
| url |
https://doi.org/10.1007/s11227-015-1431-y |
| remote_bool |
false |
| author2 |
Abu-Dalo, Ansam M. Jararweh, Yaser Jarrah, Moath Sa’d, Mohammad Al |
| author2Str |
Abu-Dalo, Ansam M. Jararweh, Yaser Jarrah, Moath Sa’d, Mohammad Al |
| ppnlink |
13046466X |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11227-015-1431-y |
| up_date |
2024-07-03T19:01:40.854Z |
| _version_ |
1803585638481526784 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC203394770X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504053856.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-015-1431-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC203394770X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11227-015-1431-y-p</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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Al-Ayyoub, Mahmoud</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Fuzzy clustering is one of the most popular techniques in medical image segmentation. The fuzzy C-means (FCM) algorithm has been widely used as it provides better performance and more information than other algorithms. As the data set becomes large, the serial implementation of the FCM algorithm becomes too slow to accomplish the clustering task within acceptable time. Hence, a parallel implementation [for example, using today’s fast graphics processing unit (GPU)] is needed. In this paper, we implement brFCM algorithm, a faster variant of the FCM algorithm, on two different GPU cards, Tesla M2070 and Tesla K20m. We compare our brFCM GPU-based implementation with its CPU-based sequential implementation. Moreover, we compare brFCM with the traditional version of the FCM algorithm. The experiments used lung CT and knee MRI images for clustering. The results show that our implementation has a significant improvement over the traditional CPU sequential implementation. GPU parallel brFCM is 2.24 times faster than its CPU implementation, and 23.43 times faster than a GPU parallel implementation of the traditional FCM.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graphics processing unit (GPU)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Medical imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy C-means (FCM) algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abu-Dalo, Ansam M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jararweh, Yaser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jarrah, Moath</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sa’d, Mohammad Al</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The journal of supercomputing</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">71(2015), 8 vom: 23. Apr., Seite 3149-3162</subfield><subfield code="w">(DE-627)13046466X</subfield><subfield code="w">(DE-600)740510-8</subfield><subfield code="w">(DE-576)018667775</subfield><subfield code="x">0920-8542</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:71</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:8</subfield><subfield code="g">day:23</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:3149-3162</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11227-015-1431-y</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">71</subfield><subfield code="j">2015</subfield><subfield code="e">8</subfield><subfield code="b">23</subfield><subfield code="c">04</subfield><subfield code="h">3149-3162</subfield></datafield></record></collection>
|
| score |
7.3991594 |