Document Type
Conference Paper
Rights
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
Disciplines
1.3 PHYSICAL SCIENCES, Optics
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).
DOI
https://doi.org/10.1109/SBFoton-IOPC.2018.8610920
Recommended Citation
C. Lima, L. Correa, H. Byrne and D. Zezell, "K-means and Hierarchical Cluster Analysis as segmentation algorithms of FTIR hyperspectral images collected from cutaneous tissue," 2018 SBFoton International Optics and Photonics Conference (SBFoton IOPC), 2018, pp. 1-4, doi: 10.1109/SBFoton-IOPC.2018.8610920.
Funder
CEPID-FAPESP; CNPq; CAPES
Publication Details
2018 SBFoton International Optics and Photonics Conference (SBFoton IOPC)