Description of field of research:

Oral cancer is one of the most common types of cancer that develops in the tissues of the mouth. Patients diagnosed at advanced stages have only a 40% chance of survival and commonly require painful and highly invasive surgery to remove parts of the oral cavity. In contrast, patients diagnosed early usually require minor surgery and have an 84% chance of survival. Therefore, early detection holds great promise for improving both the survival rate and quality of life of these patients. An optical technology named Fluorescence lifetime imaging (FLIM) endoscopy is one promising imaging modality that can capture indicative parameters to discriminate lesion tissues from normal tissues. The lesion tissues can be further categorized into (1) Benign Lesions (non-cancerous), (2) Precancerous Lesions, and (3) Cancerous Lesions. Multispectral FLIM images of lesion and healthy tissues from 125 patients were acquired for the research study.

In this project, you will be applying statistical image processing techniques and machine learning methods to discriminate the benign, precancerous, cancerous, and healthy tissues, which leads to the development of an automated framework for oral cancer diagnosis.

School

Electrical Engineering and Telecommunications

Research areas

Signal processing, machine learning

Team of 4 researchers.

The development and testing of code that can (1) extract features from the raw FLIM endoscopy images (2) discriminate the benign, precancerous, cancerous, and healthy tissues from the extracted features.

Elvis Duran-Sierra et al., Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions, Oral Oncology, Volume 105, 2020, 104635, ISSN 1368-8375, https://doi.org/10.1016/j.oraloncology.2020.104635