@inproceedings{e449afbff9a7490c9fd5dad5aa8f6989,
title = "K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue",
abstract = "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).",
keywords = "FTIR microspectroscopy, HCA, Image segmentation, KMCA",
author = "Cassio Lima and Luciana Correa and Hugh Byrne and Denise Zezell",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 1st SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018 ; Conference date: 08-10-2018 Through 10-10-2018",
year = "2019",
month = jan,
day = "11",
doi = "10.1109/SBFoton-IOPC.2018.8610920",
language = "English",
series = "2018 SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 SBFoton International Optics and Photonics Conference, SBFoton IOPC 2018",
address = "United States",
}