Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging
Psoriasis is a chronic inflammatory skin condition which affects 2-3% of population around the world. Psoriasis Area and Severity Index (PASI) is a gold standard to assess psoriasis severity as well as the treatment efficacy. Although a gold standard, PASI is rarely used because it is tedious and co...
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ftdatacite:10.5281/zenodo.1084273 2023-05-15T18:19:01+02:00 Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging M.H. Ahmad Fadzil Hurriyatul Fitriyah Prakasa, Esa Hermawan Nugroho S.H. Hussein Azura Mohd. Affandi 2010 https://dx.doi.org/10.5281/zenodo.1084273 https://zenodo.org/record/1084273 en eng Zenodo https://dx.doi.org/10.5281/zenodo.1084274 Open Access Creative Commons Attribution 4.0 https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess CC-BY 3D digital imaging base construction PASI psoriasis lesion thickness. Text Journal article article-journal ScholarlyArticle 2010 ftdatacite https://doi.org/10.5281/zenodo.1084273 https://doi.org/10.5281/zenodo.1084274 2021-11-05T12:55:41Z Psoriasis is a chronic inflammatory skin condition which affects 2-3% of population around the world. Psoriasis Area and Severity Index (PASI) is a gold standard to assess psoriasis severity as well as the treatment efficacy. Although a gold standard, PASI is rarely used because it is tedious and complex. In practice, PASI score is determined subjectively by dermatologists, therefore inter and intra variations of assessment are possible to happen even among expert dermatologists. This research develops an algorithm to assess psoriasis lesion for PASI scoring objectively. Focus of this research is thickness assessment as one of PASI four parameters beside area, erythema and scaliness. Psoriasis lesion thickness is measured by averaging the total elevation from lesion base to lesion surface. Thickness values of 122 3D images taken from 39 patients are grouped into 4 PASI thickness score using K-means clustering. Validation on lesion base construction is performed using twelve body curvature models and show good result with coefficient of determinant (R2) is equal to 1. : {"references": ["Peter van de Kerkhof, Textbook of Psoriasis, 2003, Blackwell\nPublishing: Massachussetts", "The Psoriasis Association, What is Psoriasis?, 2008, The Psoriasis\nAssociation: UK", "Lionel Fry, An Atlas of Psoriasis, 2005, Taylor&Francis: London", "T. Frederiksson, U. Pettersson, Severe Psoriasis: Oral Therapy with a\nNew Retinoid, Dermatologica, 1978, 157(4), pp: 238-44", "M. Alper, A. Kavak, A.H. Parlak, R. Demirici, I. Belenli, N. Yesildal,\nMeasurement of Epidermal Thickness in a Patient with Psoriasis by\nComputer Supported Image Analyisis, Brazilian Journal of Medical\nand Biological Research, 2004, 37, pp: 111-117.", "Harold Alexander, D.L. Miller, Determining Skin Thickness with\nPulsed Ultra Sound. The Journal of Investigative Dermatology, Vol 72,\npp: 17-19. 1979.", "Serup, J.: Non-invasive quantification of psoriasis plaques-\nmeasurement of skin thickness with 15 MHz pulsed ultrasound. Journal\nof Clinical and Experimental Dermatology, Volume 9 Issue 5, 502 --\n508 (2006)", "Konica Minolta Vivid 910 Non Contact 3D Digitizer Manual\nHandbook, Japan (2001)", "Bryan F. Jones, Peter Plassman, An Instrument to Measure the\nDimension of Skin Wounds, IEEE Trancastion on Biomedical\nEngineering, Vol. 42, No.5 1995, pp: 464 - 470\n[10] Zhilin Li, Qing Zhu, Christopher Gold, Digital Terrain Modeling:\nPrinciple and Methodology,2005, CRC Press: Florida\n[11] J. P. Luntama, S. Koponen, M. Hallikainen, Analysis of Sea Ice\nThickness and Mass Estimation with a Spaceborne Laser Altimerer,\nGeosciense and Remote Sensing, 1997, Volume 3. pp: 1314-1316\n[12] Frederic Gibou and Ronald Fedkiw. \"A fast hybrid k-means level set\nalgorithm for segmentation\". In 4th Annual Hawaii International\nConference on Statistics and Mathematics, pages 281-291, 2005.\n[13] R. Herwig, A.J. Poustka, C. Muller, C. Bull, H. Lehrach, and J O-Brien.\nLarge-scale clustering of cdna-fingerprinting data. Genome Research,\n9:1093-1105, 1999.\n[14] Paul J. Besl, Ramesh C. Jain, Three-Dimensional Object Recognition,\nAnnals of Discrete Mathematics-ACM, Vol. 17, 1985, pp: 75-145"]} Text Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
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English |
topic |
3D digital imaging base construction PASI psoriasis lesion thickness. |
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3D digital imaging base construction PASI psoriasis lesion thickness. M.H. Ahmad Fadzil Hurriyatul Fitriyah Prakasa, Esa Hermawan Nugroho S.H. Hussein Azura Mohd. Affandi Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging |
topic_facet |
3D digital imaging base construction PASI psoriasis lesion thickness. |
description |
Psoriasis is a chronic inflammatory skin condition which affects 2-3% of population around the world. Psoriasis Area and Severity Index (PASI) is a gold standard to assess psoriasis severity as well as the treatment efficacy. Although a gold standard, PASI is rarely used because it is tedious and complex. In practice, PASI score is determined subjectively by dermatologists, therefore inter and intra variations of assessment are possible to happen even among expert dermatologists. This research develops an algorithm to assess psoriasis lesion for PASI scoring objectively. Focus of this research is thickness assessment as one of PASI four parameters beside area, erythema and scaliness. Psoriasis lesion thickness is measured by averaging the total elevation from lesion base to lesion surface. Thickness values of 122 3D images taken from 39 patients are grouped into 4 PASI thickness score using K-means clustering. Validation on lesion base construction is performed using twelve body curvature models and show good result with coefficient of determinant (R2) is equal to 1. : {"references": ["Peter van de Kerkhof, Textbook of Psoriasis, 2003, Blackwell\nPublishing: Massachussetts", "The Psoriasis Association, What is Psoriasis?, 2008, The Psoriasis\nAssociation: UK", "Lionel Fry, An Atlas of Psoriasis, 2005, Taylor&Francis: London", "T. Frederiksson, U. Pettersson, Severe Psoriasis: Oral Therapy with a\nNew Retinoid, Dermatologica, 1978, 157(4), pp: 238-44", "M. Alper, A. Kavak, A.H. Parlak, R. Demirici, I. Belenli, N. Yesildal,\nMeasurement of Epidermal Thickness in a Patient with Psoriasis by\nComputer Supported Image Analyisis, Brazilian Journal of Medical\nand Biological Research, 2004, 37, pp: 111-117.", "Harold Alexander, D.L. Miller, Determining Skin Thickness with\nPulsed Ultra Sound. The Journal of Investigative Dermatology, Vol 72,\npp: 17-19. 1979.", "Serup, J.: Non-invasive quantification of psoriasis plaques-\nmeasurement of skin thickness with 15 MHz pulsed ultrasound. Journal\nof Clinical and Experimental Dermatology, Volume 9 Issue 5, 502 --\n508 (2006)", "Konica Minolta Vivid 910 Non Contact 3D Digitizer Manual\nHandbook, Japan (2001)", "Bryan F. Jones, Peter Plassman, An Instrument to Measure the\nDimension of Skin Wounds, IEEE Trancastion on Biomedical\nEngineering, Vol. 42, No.5 1995, pp: 464 - 470\n[10] Zhilin Li, Qing Zhu, Christopher Gold, Digital Terrain Modeling:\nPrinciple and Methodology,2005, CRC Press: Florida\n[11] J. P. Luntama, S. Koponen, M. Hallikainen, Analysis of Sea Ice\nThickness and Mass Estimation with a Spaceborne Laser Altimerer,\nGeosciense and Remote Sensing, 1997, Volume 3. pp: 1314-1316\n[12] Frederic Gibou and Ronald Fedkiw. \"A fast hybrid k-means level set\nalgorithm for segmentation\". In 4th Annual Hawaii International\nConference on Statistics and Mathematics, pages 281-291, 2005.\n[13] R. Herwig, A.J. Poustka, C. Muller, C. Bull, H. Lehrach, and J O-Brien.\nLarge-scale clustering of cdna-fingerprinting data. Genome Research,\n9:1093-1105, 1999.\n[14] Paul J. Besl, Ramesh C. Jain, Three-Dimensional Object Recognition,\nAnnals of Discrete Mathematics-ACM, Vol. 17, 1985, pp: 75-145"]} |
format |
Text |
author |
M.H. Ahmad Fadzil Hurriyatul Fitriyah Prakasa, Esa Hermawan Nugroho S.H. Hussein Azura Mohd. Affandi |
author_facet |
M.H. Ahmad Fadzil Hurriyatul Fitriyah Prakasa, Esa Hermawan Nugroho S.H. Hussein Azura Mohd. Affandi |
author_sort |
M.H. Ahmad Fadzil |
title |
Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging |
title_short |
Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging |
title_full |
Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging |
title_fullStr |
Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging |
title_full_unstemmed |
Objective Assessment Of Psoriasis Lesion Thickness For Pasi Scoring Using 3D Digital Imaging |
title_sort |
objective assessment of psoriasis lesion thickness for pasi scoring using 3d digital imaging |
publisher |
Zenodo |
publishDate |
2010 |
url |
https://dx.doi.org/10.5281/zenodo.1084273 https://zenodo.org/record/1084273 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.5281/zenodo.1084274 |
op_rights |
Open Access Creative Commons Attribution 4.0 https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.1084273 https://doi.org/10.5281/zenodo.1084274 |
_version_ |
1766195845596708864 |