Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

• Type of Material: Conference Paper
• Authors: Hyunkoo Chung, Georgia Institute of Technology; Guolan Lu, Georgia Institute of Technology; Zhiqiang Tian, Emory University; Dongsheng Wang, Emory University; Georgia Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2016-01-01
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2216559
• Title of Journal or Parent Work: Proceedings of SPIE
• Conference or Event Name: SPIE Biomedical Applications in Molecular, Structural and Functional Imaging Conference
• Volume: 9788
• Grant/Funding Information: This research is supported in part by NIH grants (CA176684 and CA156775).
• Keywords: Image classification; Optics; Science & Technology; Support vector machine (SVM); Feature Extraction; Superpixels; Life Sciences & Biomedicine; Physical Sciences; Head and neck cancer; Principal Component Analysis (PCA); Radiology, Nuclear Medicine & Medical Imaging; Hyperspectral Imaging
• Research Categories: Engineering, Biomedical; Health Sciences, Oncology; Health Sciences, Radiology

Abstract

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.