K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue
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2018
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SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE
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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).
Como referenciar
LIMA, CASSIO; CORREA, LUCIANA; BYRNE, HUGH; ZEZELL, DENISE. K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, October 08-10, 2018, Campinas, SP. Proceedings... Piscataway, NJ, USA: IEEE, 2018. DOI: 10.1109/SBFoton-IOPC.2018.8610920. Disponível em: http://repositorio.ipen.br/handle/123456789/29821. Acesso em: 20 Mar 2026.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.