Open Access
27 June 2020 Gastric cancer diagnosis using hyperspectral imaging with principal component analysis and spectral angle mapper
Ningliang Liu, Yaxiong Guo, Houmin Jiang, Weisong Yi
Author Affiliations +
Abstract

Significance: Hyperspectral imaging (HSI) is an emerging optical technique that has a double function of spectroscopy and imaging.

Aim: Near-infrared hyperspectral imaging (NIR-HSI) (900 to 1700 nm) with the help of chemometrics was investigated for gastric cancer diagnosis.

Approach: Mean spectra and standard deviation of normal and cancerous pixels were extracted. Principal component analysis (PCA) was used to compress the dimension of hypercube data and select the optimal wavelengths. Moreover, spectral angle mapper (SAM) was utilized as chemometrics to discriminate gastric cancer from normal.

Results: Major spectral difference of cancerous and normal gastric tissue was observed around 975, 1215, and 1450 nm by comparison. A total of six wavelengths (i.e., 975, 1075, 1215, 1275, 1390, and 1450 nm) were then selected as optimal wavelengths by PCA. The accuracy using SAM is up to 90% according to hematoxylin–eosin results.

Conclusions: These results suggest that NIR-HSI has the potential as a cutting-edge optical diagnostic technique for gastric cancer diagnosis with suitable chemometrics.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Ningliang Liu, Yaxiong Guo, Houmin Jiang, and Weisong Yi "Gastric cancer diagnosis using hyperspectral imaging with principal component analysis and spectral angle mapper," Journal of Biomedical Optics 25(6), 066005 (27 June 2020). https://doi.org/10.1117/1.JBO.25.6.066005
Received: 20 December 2019; Accepted: 12 June 2020; Published: 27 June 2020
Lens.org Logo
CITATIONS
Cited by 21 scholarly publications.
Advertisement
Advertisement
KEYWORDS
Cancer

Hyperspectral imaging

Principal component analysis

Tissues

Diagnostics

Reflectivity

Chemometrics

Back to Top