Classification of skin-cancer lesions based on fluorescence lifetime imaging

Priyanka Vasanthakumari; Renan A. Romano; Ramon G. T. Rosa; Ana G. Salvio; Vladislav Yakovlev; Cristina Kurachi; Javier A. Jo

doi:10.1117/12.2548625

28 February 2020 Classification of skin-cancer lesions based on fluorescence lifetime imaging

Priyanka Vasanthakumari, Renan A. Romano, Ramon G. T. Rosa, Ana G. Salvio, Vladislav Yakovlev, Cristina Kurachi, Javier A. Jo

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113170Z (2020) https://doi.org/10.1117/12.2548625
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Every year more than 5.4 million new cases of skin cancer are reported in the US. Melanoma is the most lethal type with only 5% occurrence rate, but accounts for over 75% of all skin cancer deaths. Non-melanoma skin cancer, especially basal cell carcinoma (BCC) is the most commonly occurring and often curable type that affects more than 3 million people and causes about 2000 deaths in the US annually. The current diagnosis involves visual inspection, followed by biopsy of the lesions. The major drawbacks of this practice include difficulty in border detection causing incomplete treatment and, the inability to distinguish between clinically similar lesions. Melanoma is often mistaken for the benign lesion pigmented seborrheic keratosis (pSK), making it extremely important to differentiate benign and malignant lesions. In this work, a novel feature extraction algorithm based on phasors was performed on the Fluorescence Lifetime Imaging (FLIM) images of the skin to reliably distinguish between benign and malignant lesions. This approach, unlike the standard FLIM data processing method that requires time-deconvolution of the instrument response from the measured time-resolved fluorescence signal, is computationally much simpler and provides a unique set of features for classification. Subsequently, FLIM derived features were selected using a double step cross validation approach that assesses the reliability and the performance of the resultant trained classifier. Promising FLIM-based classification performance was attained for detecting benign from malignant pigmented (sensitivity: ~80%, specificity: 79%, overall accuracy: ~79%) and nonpigmented (sensitivity: ~88%, specificity: 83%, overall accuracy: ~87%) lesions.

Conference Presentation

Citation Download Citation

Priyanka Vasanthakumari, Renan A. Romano, Ramon G. T. Rosa, Ana G. Salvio, Vladislav Yakovlev, Cristina Kurachi, and Javier A. Jo "Classification of skin-cancer lesions based on fluorescence lifetime imaging", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170Z (28 February 2020); https://doi.org/10.1117/12.2548625

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available