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5 March 2021 Scattering coefficient maps acquired from optical coherence tomography aid in diagnosis of colorectal abnormalities
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Abstract
In this ex vivo study, we report the first use of texture features and computer vision-based image features acquired from en face scattering coefficient maps to diagnose colorectal diseases. From these maps, texture features were extracted from a gray-level co-occurrence matrix algorithm, and computer vision-based image features were derived using a scale-invariant feature transform algorithm. Twenty-five features were obtained and thirty-three patients were recruited. Machine learning models were trained using an optimal feature set. The trained models achieved 94.7% sensitivity and 94.0% specificity for differentiating abnormal from normal, and 86.9% sensitivity and 85.0% specificity when distinguishing adenomatous polyp from cancer.
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© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yifeng Zeng, William C. Chapman Jr., Yixiao Lin, Shuying Li, and Quing Zhu "Scattering coefficient maps acquired from optical coherence tomography aid in diagnosis of colorectal abnormalities", Proc. SPIE 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV, 116300V (5 March 2021); https://doi.org/10.1117/12.2576908
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