Open AccessIdentification of oral squamous cell carcinoma in optical coherence tomography images based on texture features
Author(s) -
Zihan Yang,
Jun Shang,
Chenlu Liu,
Jun Zhang,
Yanmei Liang
Publication year - 2020
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545821400010
Subject(s) - optical coherence tomography , artificial intelligence , basal cell , texture (cosmology) , medicine , pattern recognition (psychology) , computer science , classifier (uml) , computer vision , radiology , pathology , image (mathematics)
Surgical excision is an effective treatment for oral squamous cell carcinoma (OSCC), but exact intraoperative differentiation OSCC from the normal tissue is the first premise. As a noninvasive imaging technique, optical coherence tomography (OCT) has the nearly same resolution as the histopathological examination, whose images contain rich information to assist surgeons to make clinical decisions. We extracted kinds of texture features from OCT images obtained by a home-made swept-source OCT system in this paper, and studied the identification of OSCC based on different combinations of texture features and machine learning classifiers. It was demonstrated that different combinations had different accuracies, among which the combination of texture features, gray level co-occurrence matrix (GLCM), Laws’ texture measures (LM), and center symmetric auto-correlation (CSAC), and SVM as the classifier, had the optimal comprehensive identification effect, whose accuracy was 94.1%. It was proven that it is feasible to distinguish OSCC based on texture features in OCT images, and it has great potential in helping surgeons make rapid and accurate decisions in oral clinical practice.