Next Article in Journal
Construction of a Ginseng Root-Meristem Sensor and a Sensing Kinetics Study on the Main Nitrogen Nutrients
Previous Article in Journal
Muscle Co-Activation around the Knee during Different Walking Speeds in Healthy Females
Previous Article in Special Issue
Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver
Open AccessArticle

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing

1
Department of Education and Pedagogy, UiT the Arctic University of Norway, 9019 Tromsø, Norway
2
Institute for Applied Microelectronics, Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
3
Department of Dermatology, Hospital Universitario de Gran Canaria Doctor Negrín, 35016 Las Palmas de Gran Canaria, Spain
4
Department of Dermatology, Complejo Hospitalario Universitario Insular-Materno Infantil, 35016 Las Palmas de Gran Canaria, Spain
5
Department of Electromedicine, Complejo Hospitalario Universitario Insular-Materno Infantil, 35016 Las Palmas de Gran Canaria, Spain
6
Department of Mathematics and Statistics, UiT the Arctic University of Norway, 9019 Tromsø, Norway
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 680; https://doi.org/10.3390/s21030680
Received: 15 December 2020 / Revised: 13 January 2021 / Accepted: 15 January 2021 / Published: 20 January 2021
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
This paper shows new contributions in the detection of skin cancer, where we present the use of a customized hyperspectral system that captures images in the spectral range from 450 to 950 nm. By choosing a 7 × 7 sub-image of each channel in the hyperspectral image (HSI) and then taking the mean and standard deviation of these sub-images, we were able to make fits of the resulting curves. These fitted curves had certain characteristics, which then served as a basis of classification. The most distinct fit was for the melanoma pigmented skin lesions (PSLs), which is also the most aggressive malignant cancer. Furthermore, we were able to classify the other PSLs in malignant and benign classes. This gives us a rather complete classification method for PSLs with a novel perspective of the classification procedure by exploiting the variability of each channel in the HSI. View Full-Text
Keywords: hyperspectral; curve fit; statistical discrimination; melanoma; benign; malignant hyperspectral; curve fit; statistical discrimination; melanoma; benign; malignant
Show Figures

Figure 1

MDPI and ACS Style

Uteng, S.; Quevedo, E.; M. Callico, G.; Castaño, I.; Carretero, G.; Almeida, P.; Garcia, A.; A. Hernandez, J.; Godtliebsen, F. Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing. Sensors 2021, 21, 680. https://doi.org/10.3390/s21030680

AMA Style

Uteng S, Quevedo E, M. Callico G, Castaño I, Carretero G, Almeida P, Garcia A, A. Hernandez J, Godtliebsen F. Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing. Sensors. 2021; 21(3):680. https://doi.org/10.3390/s21030680

Chicago/Turabian Style

Uteng, Stig; Quevedo, Eduardo; M. Callico, Gustavo; Castaño, Irene; Carretero, Gregorio; Almeida, Pablo; Garcia, Aday; A. Hernandez, Javier; Godtliebsen, Fred. 2021. "Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing" Sensors 21, no. 3: 680. https://doi.org/10.3390/s21030680

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop