Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images
Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lazareva, Anfisa [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of the Optical Society of America / A - Washington, DC : Soc., 1984, 33(2016), 1, Seite 84 |
|---|---|
| Übergeordnetes Werk: |
volume:33 ; year:2016 ; number:1 ; pages:84 |
| Links: |
|---|
| Katalog-ID: |
OLC1974012204 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1974012204 | ||
| 003 | DE-627 | ||
| 005 | 20230714185430.0 | ||
| 007 | tu | ||
| 008 | 160430s2016 xx ||||| 00| ||eng c | ||
| 028 | 5 | 2 | |a PQ20160430 |
| 035 | |a (DE-627)OLC1974012204 | ||
| 035 | |a (DE-599)GBVOLC1974012204 | ||
| 035 | |a (PRQ)pubmed_primary_268315890 | ||
| 035 | |a (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 530 |q DNB |
| 100 | 1 | |a Lazareva, Anfisa |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. | ||
| 700 | 1 | |a Liatsis, Panos |4 oth | |
| 700 | 1 | |a Rauscher, Franziska G |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of the Optical Society of America / A |d Washington, DC : Soc., 1984 |g 33(2016), 1, Seite 84 |w (DE-627)129862142 |w (DE-600)283633-6 |w (DE-576)015173496 |x 1084-7529 |7 nnns |
| 773 | 1 | 8 | |g volume:33 |g year:2016 |g number:1 |g pages:84 |
| 856 | 4 | 2 | |u http://www.ncbi.nlm.nih.gov/pubmed/26831589 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-PHY | ||
| 912 | |a SSG-OPC-BBI | ||
| 912 | |a SSG-OPC-AST | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_150 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_2006 | ||
| 912 | |a GBV_ILN_2286 | ||
| 912 | |a GBV_ILN_4305 | ||
| 951 | |a AR | ||
| 952 | |d 33 |j 2016 |e 1 |h 84 | ||
| author_variant |
a l al |
|---|---|
| matchkey_str |
article:10847529:2016----::esalgitrnfrnacmnadeetoopooeetrelia |
| hierarchy_sort_str |
2016 |
| publishDate |
2016 |
| allfields |
PQ20160430 (DE-627)OLC1974012204 (DE-599)GBVOLC1974012204 (PRQ)pubmed_primary_268315890 (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho DE-627 ger DE-627 rakwb eng 530 DNB Lazareva, Anfisa verfasserin aut Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. Liatsis, Panos oth Rauscher, Franziska G oth Enthalten in Journal of the Optical Society of America / A Washington, DC : Soc., 1984 33(2016), 1, Seite 84 (DE-627)129862142 (DE-600)283633-6 (DE-576)015173496 1084-7529 nnns volume:33 year:2016 number:1 pages:84 http://www.ncbi.nlm.nih.gov/pubmed/26831589 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OPC-BBI SSG-OPC-AST GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_150 GBV_ILN_170 GBV_ILN_2006 GBV_ILN_2286 GBV_ILN_4305 AR 33 2016 1 84 |
| spelling |
PQ20160430 (DE-627)OLC1974012204 (DE-599)GBVOLC1974012204 (PRQ)pubmed_primary_268315890 (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho DE-627 ger DE-627 rakwb eng 530 DNB Lazareva, Anfisa verfasserin aut Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. Liatsis, Panos oth Rauscher, Franziska G oth Enthalten in Journal of the Optical Society of America / A Washington, DC : Soc., 1984 33(2016), 1, Seite 84 (DE-627)129862142 (DE-600)283633-6 (DE-576)015173496 1084-7529 nnns volume:33 year:2016 number:1 pages:84 http://www.ncbi.nlm.nih.gov/pubmed/26831589 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OPC-BBI SSG-OPC-AST GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_150 GBV_ILN_170 GBV_ILN_2006 GBV_ILN_2286 GBV_ILN_4305 AR 33 2016 1 84 |
| allfields_unstemmed |
PQ20160430 (DE-627)OLC1974012204 (DE-599)GBVOLC1974012204 (PRQ)pubmed_primary_268315890 (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho DE-627 ger DE-627 rakwb eng 530 DNB Lazareva, Anfisa verfasserin aut Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. Liatsis, Panos oth Rauscher, Franziska G oth Enthalten in Journal of the Optical Society of America / A Washington, DC : Soc., 1984 33(2016), 1, Seite 84 (DE-627)129862142 (DE-600)283633-6 (DE-576)015173496 1084-7529 nnns volume:33 year:2016 number:1 pages:84 http://www.ncbi.nlm.nih.gov/pubmed/26831589 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OPC-BBI SSG-OPC-AST GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_150 GBV_ILN_170 GBV_ILN_2006 GBV_ILN_2286 GBV_ILN_4305 AR 33 2016 1 84 |
| allfieldsGer |
PQ20160430 (DE-627)OLC1974012204 (DE-599)GBVOLC1974012204 (PRQ)pubmed_primary_268315890 (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho DE-627 ger DE-627 rakwb eng 530 DNB Lazareva, Anfisa verfasserin aut Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. Liatsis, Panos oth Rauscher, Franziska G oth Enthalten in Journal of the Optical Society of America / A Washington, DC : Soc., 1984 33(2016), 1, Seite 84 (DE-627)129862142 (DE-600)283633-6 (DE-576)015173496 1084-7529 nnns volume:33 year:2016 number:1 pages:84 http://www.ncbi.nlm.nih.gov/pubmed/26831589 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OPC-BBI SSG-OPC-AST GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_150 GBV_ILN_170 GBV_ILN_2006 GBV_ILN_2286 GBV_ILN_4305 AR 33 2016 1 84 |
| allfieldsSound |
PQ20160430 (DE-627)OLC1974012204 (DE-599)GBVOLC1974012204 (PRQ)pubmed_primary_268315890 (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho DE-627 ger DE-627 rakwb eng 530 DNB Lazareva, Anfisa verfasserin aut Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. Liatsis, Panos oth Rauscher, Franziska G oth Enthalten in Journal of the Optical Society of America / A Washington, DC : Soc., 1984 33(2016), 1, Seite 84 (DE-627)129862142 (DE-600)283633-6 (DE-576)015173496 1084-7529 nnns volume:33 year:2016 number:1 pages:84 http://www.ncbi.nlm.nih.gov/pubmed/26831589 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OPC-BBI SSG-OPC-AST GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_150 GBV_ILN_170 GBV_ILN_2006 GBV_ILN_2286 GBV_ILN_4305 AR 33 2016 1 84 |
| language |
English |
| source |
Enthalten in Journal of the Optical Society of America / A 33(2016), 1, Seite 84 volume:33 year:2016 number:1 pages:84 |
| sourceStr |
Enthalten in Journal of the Optical Society of America / A 33(2016), 1, Seite 84 volume:33 year:2016 number:1 pages:84 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| dewey-raw |
530 |
| isfreeaccess_bool |
false |
| container_title |
Journal of the Optical Society of America / A |
| authorswithroles_txt_mv |
Lazareva, Anfisa @@aut@@ Liatsis, Panos @@oth@@ Rauscher, Franziska G @@oth@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
129862142 |
| dewey-sort |
3530 |
| id |
OLC1974012204 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1974012204</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714185430.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160430s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1974012204</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1974012204</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)pubmed_primary_268315890</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho</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">530</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lazareva, Anfisa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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="520" ind1=" " ind2=" "><subfield code="a">Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liatsis, Panos</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rauscher, Franziska G</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of the Optical Society of America / A</subfield><subfield code="d">Washington, DC : Soc., 1984</subfield><subfield code="g">33(2016), 1, Seite 84</subfield><subfield code="w">(DE-627)129862142</subfield><subfield code="w">(DE-600)283633-6</subfield><subfield code="w">(DE-576)015173496</subfield><subfield code="x">1084-7529</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:33</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:84</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/26831589</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-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-AST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2286</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">33</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="h">84</subfield></datafield></record></collection>
|
| author |
Lazareva, Anfisa |
| spellingShingle |
Lazareva, Anfisa ddc 530 Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| authorStr |
Lazareva, Anfisa |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129862142 |
| format |
Article |
| dewey-ones |
530 - Physics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1084-7529 |
| topic_title |
530 DNB Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| topic |
ddc 530 |
| topic_unstemmed |
ddc 530 |
| topic_browse |
ddc 530 |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
p l pl f g r fg fgr |
| hierarchy_parent_title |
Journal of the Optical Society of America / A |
| hierarchy_parent_id |
129862142 |
| dewey-tens |
530 - Physics |
| hierarchy_top_title |
Journal of the Optical Society of America / A |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129862142 (DE-600)283633-6 (DE-576)015173496 |
| title |
Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| ctrlnum |
(DE-627)OLC1974012204 (DE-599)GBVOLC1974012204 (PRQ)pubmed_primary_268315890 (KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho |
| title_full |
Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| author_sort |
Lazareva, Anfisa |
| journal |
Journal of the Optical Society of America / A |
| journalStr |
Journal of the Optical Society of America / A |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
txt |
| container_start_page |
84 |
| author_browse |
Lazareva, Anfisa |
| container_volume |
33 |
| class |
530 DNB |
| format_se |
Aufsätze |
| author-letter |
Lazareva, Anfisa |
| dewey-full |
530 |
| title_sort |
hessian-log filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| title_auth |
Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| abstract |
Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. |
| abstractGer |
Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. |
| abstract_unstemmed |
Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OPC-BBI SSG-OPC-AST GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_70 GBV_ILN_150 GBV_ILN_170 GBV_ILN_2006 GBV_ILN_2286 GBV_ILN_4305 |
| container_issue |
1 |
| title_short |
Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images |
| url |
http://www.ncbi.nlm.nih.gov/pubmed/26831589 |
| remote_bool |
false |
| author2 |
Liatsis, Panos Rauscher, Franziska G |
| author2Str |
Liatsis, Panos Rauscher, Franziska G |
| ppnlink |
129862142 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| up_date |
2024-07-04T03:36:06.418Z |
| _version_ |
1803618003374309376 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1974012204</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714185430.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160430s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1974012204</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1974012204</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)pubmed_primary_268315890</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0136893120160000033000100084hessianlogfilteringforenhancementanddetectionofpho</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">530</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lazareva, Anfisa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hessian-LoG filtering for enhancement and detection of photoreceptor cells in adaptive optics retinal images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</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="520" ind1=" " ind2=" "><subfield code="a">Automated analysis of retinal images plays a vital role in the examination, diagnosis, and prognosis of healthy and pathological retinas. Retinal disorders and the associated visual loss can be interpreted via quantitative correlations, based on measurements of photoreceptor loss. Therefore, it is important to develop reliable tools for identification of photoreceptor cells. In this paper, an automated algorithm is proposed, based on the use of the Hessian-Laplacian of Gaussian filter, which allows enhancement and detection of photoreceptor cells. The performance of the proposed technique is evaluated on both synthetic and high-resolution retinal images, in terms of packing density. The results on the synthetic data were compared against ground truth as well as cone counts obtained by the Li and Roorda algorithm. For the synthetic datasets, our method showed an average detection accuracy of 98.8%, compared to 93.9% for the Li and Roorda approach. The packing density estimates calculated on the retinal datasets were validated against manual counts and the results obtained by a proprietary software from Imagine Eyes and the Li and Roorda algorithm. Among the tested methods, the proposed approach showed the closest agreement with manual counting.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liatsis, Panos</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rauscher, Franziska G</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of the Optical Society of America / A</subfield><subfield code="d">Washington, DC : Soc., 1984</subfield><subfield code="g">33(2016), 1, Seite 84</subfield><subfield code="w">(DE-627)129862142</subfield><subfield code="w">(DE-600)283633-6</subfield><subfield code="w">(DE-576)015173496</subfield><subfield code="x">1084-7529</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:33</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:84</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/26831589</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-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-AST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2286</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">33</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="h">84</subfield></datafield></record></collection>
|
| score |
7.399806 |