K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Título da fonte: | Repositório Institucional do IPEN |
Texto Completo: | http://repositorio.ipen.br/handle/123456789/29821 |
Resumo: | Fourier Transform Infrared (FTIR) spectroscopy is a rapid and label-free analytical technique whose potential as a diagnostic tool has been well demonstrated. The combination of spectroscopy and microscopy technologies enable wide-field scanning of a sample, providing a hyperspectral image with tens of thousands of spectra in a few minutes. In order to increase the information content of FTIR images, different clustering algorithms have been proposed as segmentation methods. However, systematic comparative tests of these techniques are still missing. Thus, the present paper aims to compare the ability of K-means Cluster Analysis (KMCA) and Hierarchical Cluster Analysis (HCA) as clustering algorithms to reconstruct FTIR hyperspectral images. Spectra for cluster analysis were acquired from healthy cutaneous tissue and the pseudo-color reconstructed images were compared to standard histopathology in order to assess the number of clusters required by both methods to correctly identify the morphological skin components (stratum corneum, epithelium, dermis and hypodermis). |
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2019-04-01T14:12:20Z2019-04-01T14:12:20ZOctober 08-10, 2018http://repositorio.ipen.br/handle/123456789/2982110.1109/SBFoton-IOPC.2018.8610920Fourier Transform Infrared (FTIR) spectroscopy is a rapid and label-free analytical technique whose potential as a diagnostic tool has been well demonstrated. The combination of spectroscopy and microscopy technologies enable wide-field scanning of a sample, providing a hyperspectral image with tens of thousands of spectra in a few minutes. In order to increase the information content of FTIR images, different clustering algorithms have been proposed as segmentation methods. However, systematic comparative tests of these techniques are still missing. Thus, the present paper aims to compare the ability of K-means Cluster Analysis (KMCA) and Hierarchical Cluster Analysis (HCA) as clustering algorithms to reconstruct FTIR hyperspectral images. Spectra for cluster analysis were acquired from healthy cutaneous tissue and the pseudo-color reconstructed images were compared to standard histopathology in order to assess the number of clusters required by both methods to correctly identify the morphological skin components (stratum corneum, epithelium, dermis and hypodermis).Submitted by Pedro Silva Filho (pfsilva@ipen.br) on 2019-04-01T14:12:20Z No. of bitstreams: 1 25606.pdf: 724766 bytes, checksum: 95546001304e744578e8df199da5c120 (MD5)Made available in DSpace on 2019-04-01T14:12:20Z (GMT). No. of bitstreams: 1 25606.pdf: 724766 bytes, checksum: 95546001304e744578e8df199da5c120 (MD5)Funda????o de Amparo ?? Pesquisa do Estado de S??o Paulo (FAPESP)Conselho Nacional de Desenvolvimento Cient??fico e Tecnol??gico (CNPq)Coordena????o de Aperfei??oamento de Pessoal de N??vel Superior (CAPES)FAPESP: 05/51689-2CAPES: PROCAD 88881.068505/2014-01; PDSE 88881.132771/2016-01CNPq: INCT 465763/2014-6; PQ 309902/2017-7; PhD grant 141629/2015-0IEEEK-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissueinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectSBFoton IOPCIPiscataway, NJ, USACampinas, SPLIMA, CASSIOCORREA, LUCIANABYRNE, HUGHZEZELL, DENISESBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCEinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do IPENinstname:Instituto de Pesquisas Energéticas e Nucleares (IPEN)instacron:IPEN256062018LIMA, CASSIOZEZELL, DENISE19-04Proceedings11396693LIMA, CASSIO:11396:920:SZEZELL, DENISE:693:920:NORIGINAL25606.pdf25606.pdfapplication/pdf724766http://repositorio.ipen.br/bitstream/123456789/29821/1/25606.pdf95546001304e744578e8df199da5c120MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.ipen.br/bitstream/123456789/29821/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/298212019-04-01 14:12:20.08oai:repositorio.ipen.br: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Repositório InstitucionalPUBhttp://repositorio.ipen.br/oai/requestbibl@ipen.bropendoar:45102019-04-01T14:12:20Repositório Institucional do IPEN - Instituto de Pesquisas Energéticas e Nucleares (IPEN)false |
dc.title.pt_BR.fl_str_mv |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
title |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
spellingShingle |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue LIMA, CASSIO |
title_short |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
title_full |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
title_fullStr |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
title_full_unstemmed |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
title_sort |
K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue |
author |
LIMA, CASSIO |
author_facet |
LIMA, CASSIO CORREA, LUCIANA BYRNE, HUGH ZEZELL, DENISE SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE |
author_role |
author |
author2 |
CORREA, LUCIANA BYRNE, HUGH ZEZELL, DENISE SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
LIMA, CASSIO CORREA, LUCIANA BYRNE, HUGH ZEZELL, DENISE SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE |
description |
Fourier Transform Infrared (FTIR) spectroscopy is a rapid and label-free analytical technique whose potential as a diagnostic tool has been well demonstrated. The combination of spectroscopy and microscopy technologies enable wide-field scanning of a sample, providing a hyperspectral image with tens of thousands of spectra in a few minutes. In order to increase the information content of FTIR images, different clustering algorithms have been proposed as segmentation methods. However, systematic comparative tests of these techniques are still missing. Thus, the present paper aims to compare the ability of K-means Cluster Analysis (KMCA) and Hierarchical Cluster Analysis (HCA) as clustering algorithms to reconstruct FTIR hyperspectral images. Spectra for cluster analysis were acquired from healthy cutaneous tissue and the pseudo-color reconstructed images were compared to standard histopathology in order to assess the number of clusters required by both methods to correctly identify the morphological skin components (stratum corneum, epithelium, dermis and hypodermis). |
publishDate |
2019 |
dc.date.evento.pt_BR.fl_str_mv |
October 08-10, 2018 |
dc.date.accessioned.fl_str_mv |
2019-04-01T14:12:20Z |
dc.date.available.fl_str_mv |
2019-04-01T14:12:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/conferenceObject |
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conferenceObject |
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publishedVersion |
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http://repositorio.ipen.br/handle/123456789/29821 |
dc.identifier.doi.pt_BR.fl_str_mv |
10.1109/SBFoton-IOPC.2018.8610920 |
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http://repositorio.ipen.br/handle/123456789/29821 |
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10.1109/SBFoton-IOPC.2018.8610920 |
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openAccess |
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I |
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IEEE |
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IEEE |
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