<i>In vivo</i>use of hyperspectral imaging to develop a noncontact endoscopic diagnosis support system for malignant colorectal tumors

Han, Zhen; Zhang, Aoyu; Wang, Xi-guang; Sun, Zongxiao; Wang, May D.; Xie, Tianyu

doi:10.1117/1.jbo.21.1.016001

Cited by 71 publications

(73 citation statements)

References 32 publications

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“…In particular, HSI has started to achieve promising results in the recent years with respect to cancer detection through the utilization of cutting-edge machine-learning algorithms [4,[19][20][21]. Several types of cancer have been investigated using HSI including both in vivo and ex vivo tissue samples, such as gastric and colon cancer [22][23][24][25], breast cancer [26,27], head and neck cancer [28][29][30][31][32][33], and brain cancer [34][35][36], among others.…”

Section: Introductionmentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440. 5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

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Section: Introductionmentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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“…As suggested above, an SRDA could therefore be integrated as a "chip-on-tip" at the distal end of a commercial endoscope in future. This is beneficial when compared to multispectral systems based on filter wheels 21,30,31 and tunable filters, 23,32 which sacrifice temporal resolution to sequentially acquire spectral data. Sequential acquisition of spectral data can also introduce motion artifacts between sequentially acquired spectral band images.…”

Section: Discussionmentioning

confidence: 99%

“…Methods with a finer sampling of the spectral response, such as multi-or hyperspectral imaging (MSI/HSI) systems (≈10 and 100 spectral color channels, respectively), 12,13 may enable quantitative assessment of functional tissue properties and more detailed statistical classification of the spectral changes that occur during disease. The use of reflectance-based MSI/HSI systems in biomedical applications has, for example, been shown to increase contrast in vascular conditions, 14 wound healing, [15][16][17] ophthalmology, 15,18,19 cancer diagnostics, 12,[20][21][22][23] and for the determination of tumor resection margins. 24 The application of a targeted fluorescent contrast agent is often referred to as optical molecular imaging (OMI) and has shown promise for endoscopic cancer detection in the GI tract.…”

Section: Introductionmentioning

confidence: 99%

“…Spectral imaging is typically achieved by filtering the light on either the illumination or detection side. Filtering has been achieved by the use of: a set of dichroic beamsplitters and dedicated detectors; 10,27,29 a filter wheel with a set of bandpass filters; 21,30,31 tunable filters; 23,32 or a monochromator. 33 Alternatively, a set of laser lines may be used for illumination.…”

Section: Introductionmentioning

confidence: 99%

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Bimodal reflectance and fluorescence multispectral endoscopy based on spectrally resolving detector arrays

et al. 2018

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Emerging clinical interest in combining standard white light endoscopy with targeted near-infrared (NIR) fluorescent contrast agents for improved early cancer detection has created demand for multimodal imaging endoscopes. We used two spectrally resolving detector arrays (SRDAs) to realize a bimodal endoscope capable of simultaneous reflectance-based imaging in the visible spectral region and multiplexed fluorescencebased imaging in the NIR. The visible SRDA was composed of 16 spectral bands, with peak wavelengths in the range of 463 to 648 nm and full-width at half-maximum (FWHM) between 9 and 26 nm. The NIR SRDA was composed of 25 spectral bands, with peak wavelengths in the range 659 to 891 nm and FWHM 7 to 15 nm. The spectral endoscope design was based on a "babyscope" model using a commercially available imaging fiber bundle. We developed a spectral transmission model to select optical components and provide reference endmembers for linear spectral unmixing of the recorded image data. The technical characterization of the spectral endoscope is presented, including evaluation of the angular field-of-view, barrel distortion, spatial resolution and spectral fidelity, which showed encouraging performance. An agarose phantom containing oxygenated and deoxygenated blood with three fluorescent dyes was then imaged. After spectral unmixing, the different chemical components of the phantom could be successfully identified via majority decision with high signal-to-background ratio (>3). Imaging performance was further assessed in an ex vivo porcine esophagus model. Our preliminary imaging results demonstrate the capability to simultaneously resolve multiple biological components using a compact spectral endoscopy system.

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“…This system is described in detail in Ref. 16. Twenty-seven sequential narrowband bandpass interference filters are centered at wavelengths between 405 and 665 nm in 10 nm intervals with full widths at half maximum (FWHMs) of 10 nm and ∼75% peak transmission (Shenyang HB Optical Technology Co., Ltd., China) and, together with two all-pass holes, are mounted in two motorized filter wheels positioned in the collimated light beam path.…”

mentioning

confidence: 99%

Image enhancement based onin vivohyperspectral gastroscopic images: a case study

Han

Yao

et al. 2016

J. Biomed. Opt

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Abstract. Hyperspectral imaging (HSI) has been recognized as a powerful tool for noninvasive disease detection in the gastrointestinal field. However, most of the studies on HSI in this field have involved ex vivo biopsies or resected tissues. We proposed an image enhancement method based on in vivo hyperspectral gastroscopic images. First, we developed a flexible gastroscopy system capable of obtaining in vivo hyperspectral images of different types of stomach disease mucosa. Then, depending on a specific object, an appropriate band selection algorithm based on dependence of information was employed to determine a subset of spectral bands that would yield useful spatial information. Finally, these bands were assigned to be the color components of an enhanced image of the object. A gastric ulcer case study demonstrated that our method yields higher color tone contrast, which enhanced the displays of the gastric ulcer regions, and that it will be valuable in clinical applications.

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In vivouse of hyperspectral imaging to develop a noncontact endoscopic diagnosis support system for malignant colorectal tumors

Cited by 71 publications

References 32 publications

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Bimodal reflectance and fluorescence multispectral endoscopy based on spectrally resolving detector arrays

Image enhancement based onin vivohyperspectral gastroscopic images: a case study

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