Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging

Guolan Lu; Xulei Qin; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei

doi:10.1117/12.2082284

18 March 2015 Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging

Guolan Lu, Xulei Qin, Dongsheng Wang, Zhuo Georgia Chen, Baowei Fei

Author Affiliations +

Proceedings Volume 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling; 94151B (2015) https://doi.org/10.1117/12.2082284
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Complete surgical removal of tumor tissue is essential for postoperative prognosis after surgery. Intraoperative tumor imaging and visualization are an important step in aiding surgeons to evaluate and resect tumor tissue in real time, thus enabling more complete resection of diseased tissue and better conservation of healthy tissue. As an emerging modality, hyperspectral imaging (HSI) holds great potential for comprehensive and objective intraoperative cancer assessment. In this paper, we explored the possibility of intraoperative tumor detection and visualization during surgery using HSI in the wavelength range of 450 nm - 900 nm in an animal experiment. We proposed a new algorithm for glare removal and cancer detection on surgical hyperspectral images, and detected the tumor margins in five mice with an average sensitivity and specificity of 94.4% and 98.3%, respectively. The hyperspectral imaging and quantification method have the potential to provide an innovative tool for image-guided surgery.

Citation Download Citation

Guolan Lu, Xulei Qin, Dongsheng Wang, Zhuo Georgia Chen, and Baowei Fei "Quantitative wavelength analysis and image classification for intraoperative cancer diagnosis with hyperspectral imaging", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 94151B (18 March 2015); https://doi.org/10.1117/12.2082284

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