Hyperspectral fluorescence image analysis for use in medical diagnostics

Kong, Seong G.; Du, Ziyun; Martin, Matthew; Vo‐Dinh, Tuan

doi:10.1117/12.596463

Cited by 16 publications

(17 citation statements)

References 10 publications

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“…Due to its strong theoretical foundation, good generalization capabilities, low sensitivity to the curse of dimensionality, and ability to find global classification solutions, SVM is usually preferred by many researchers over other classification paradigms for classifying hyperspectral images [20]. In the medical field, SVM has been widely used to classify several types of cancer, like prostate [21], lung tissue and lymph nodes [22] skin tumours [23][24], tongue [25] or colon [26]. In this research work LIBSVM [27], integrated software for support vector classification has been employed.…”

Section: Classification Frameworkmentioning

confidence: 99%

HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

Fabelo

Ortega

Kabwama

et al. 2016

Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016

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Hyperspectral images allow obtaining large amounts of information about the surface of the scene that is captured by the sensor. Using this information and a set of complex classification algorithms is possible to determine which material or substance is located in each pixel. The HELICoiD (HypErspectraL Imaging Cancer Detection) project is a European FET project that has the goal to develop a demonstrator capable to discriminate, with high precision, between normal and tumour tissues, operating in real-time, during neurosurgical operations. This demonstrator could help the neurosurgeons in the process of brain tumour resection, avoiding the excessive extraction of normal tissue and unintentionally leaving small remnants of tumour. Such precise delimitation of the tumour boundaries will improve the results of the surgery. The HELICoiD demonstrator is composed of two hyperspectral cameras obtained from Headwall. The first one in the spectral range from 400 to 1000 nm (visible and near infrared) and the second one in the spectral range from 900 to 1700 nm (near infrared). The demonstrator also includes an illumination system that covers the spectral range from 400 nm to 2200 nm. A data processing unit is in charge of managing all the parts of the demonstrator, and a high performance platform aims to accelerate the hyperspectral image classification process. Each one of these elements is installed in a customized structure specially designed for surgical environments. Preliminary results of the classification algorithms offer high accuracy (over 95%) in the discrimination between normal and tumour tissues.

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Section: Classification Frameworkmentioning

confidence: 99%

HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

Fabelo

Ortega

Kabwama

et al. 2016

Hyperspectral Imaging Sensors: Innovative Applications and Sensor Standards 2016

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“…187 Some other studies include determining skin ulcers, 188 and spectral imaging of cancer skin fluorescence and reflectance. 5,16,22 In vivo confocal scanning laser microscopy has also been used to analyze the skin annexes, as well as cutaneous cells from different epidermal layers by both reflectance and fluorescence mode. 189 More recent studies focused on 3-D HSI on small human skin areas, 68 quantification of skin oxygen saturation and its relation to the traditional measures of wound healing, 123 subcutaneous adipose tissue observation, 176 ischemic wound assessment, 136 and breast tumors.…”

Section: In Vivo Spectral Imagingmentioning

confidence: 99%

Review of spectral imaging technology in biomedical engineering: achievements and challenges

et al. 2013

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Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.

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“…Hyperspectral imaging (HSI) has been widely applied in various fields, such as astronomy [1,2], food quality and safety control [3,4], remote sensing [5,6], pharmaceuticals [7][8][9] and much more, due to its outstanding ability to distinguish different molecular compositions. In recent years, this technique has also gathered a huge amount of attention in biomedical fields [10][11][12][13][14][15][16][17][18]. With its ability to obtain a hypercube containing two-dimensional spatial information, along with additional spectral information of the target specimen, the combination of imaging and spectroscopy has made such technique emerge as a useful imaging modality for biomedical applications.…”

Section: Introductionmentioning

confidence: 99%

Line-scanning hyperspectral imaging based on structured illumination optical sectioning

Hsu

Chen

Huang

et al. 2017

Biomed. Opt. Express

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Line-scanning hyperspectral imaging (LHSI) is known to have a higher acquisition rate but lower sectioning capability than point-scanning hyperspectral imaging. To further increase the axial imaging contrast of LHSI, structured illumination was integrated into line excitation to remove the off-focus and scattered on-focus fluorescence signals. In an unsectioned leaf, the imaging contrast can be enhanced by 8 times, while in sectioned mouse skin tissues, a 4.5-fold enhancement can be achieved. With a spectral resolution of 1.15 nm, the fluorophores with seriously-overlapped spectra was proved to be separated without cross-talk by applying linear unmixing to the recorded spectral information.

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Hyperspectral fluorescence image analysis for use in medical diagnostics

Cited by 16 publications

References 10 publications

HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

HELICoiD project: a new use of hyperspectral imaging for brain cancer detection in real-time during neurosurgical operations

Review of spectral imaging technology in biomedical engineering: achievements and challenges

Line-scanning hyperspectral imaging based on structured illumination optical sectioning

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