Spectral-spatial classification using tensor modeling for cancer detection with hyperspectral imaging

et al. 2015

SPIE Proceedings

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Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization has been applied for decomposing hyperspectral images into physiologically meaningful chromophore concentration maps. The approach has been validated by computer-simulated hyperspectral images and in vivo tumor hyperspectral images from a head and neck cancer animal model.

Section: Methodsmentioning

confidence: 99%

Estimation of tissue optical parameters with hyperspectral imaging and spectral unmixing

Qin

et al. 2015

SPIE Proceedings

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“…These methods treat each pixel as separate measurement taken without taking into account the spatial information. To incorporate both spectral information from a pixel and its neighborhood, a spectral-spatial tensor based classification method was developed to improve classification accuracy [25, 26]. Inspired by the classification method proposed for earth surface exploration [27], a minimum spanning forest (MSF) was proposed by our group to classify cancer and healthy tissue on medical hyperspectral images [28].…”

Section: Introductionmentioning

confidence: 99%

A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging

Pike

IEEE Trans. Biomed. Eng.

et al. 2016

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Goal The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine (SVM) classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.

“…Since human vision is limited to visible light, spectral data in the near-infrared region may augment the surgeon's ability to noninvasively identify tumors with increased penetration depth into tissue. Our preliminary results show that HSI can differentiate cancerous from normal tissue noninvasively for both head and neck cancer [4] [5] [6] [7] and prostate cancer [8] [9] in animal models.…”

Section: Introductionmentioning

confidence: 99%

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

Qin

et al. 2015

SPIE Proceedings

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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.