New method for detection of gastric cancer by hyperspectral imaging: a pilot study

Kiyotoki, Shuu; Nishikawa, Jun; Okamoto, Takeshi; Hamabe, Kouichi; Saito, Mari; Goto, Atsushi; Fujita, Yusuke; Hamamoto, Yoshihiko; Takeuchi, Yusuke; Satori, Shin; Sakaida, Isao

doi:10.1117/1.jbo.18.2.026010

Cited by 102 publications

(88 citation statements)

References 14 publications

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“…The individual variation results in the difficulty of diagnosis. Consequently, it is necessary to examine the color differences between cancerous areas and non-cancerous areas as the reference in individual patients [22].…”

Section: Discussionmentioning

confidence: 99%

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Ogawa

Nishikawa

Hideura

et al. 2018

J Gastrointest Canc

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Purpose The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences. Methods Eighteen histopathologically confirmed early GC lesions were examined. We prepared images from white light endoscopy (WL), indigo carmine (Indigo), and acetic acid-indigo carmine chromoendoscopy (AIM). A border between cancerous and non-cancerous areas on endoscopic images was established from post-treatment pathological findings, and 2000 pixels with equivalent luminance values were randomly extracted from each image of cancerous and non-cancerous areas. Each pixel was represented as a three-dimensional vector with RGB values and defined as a sample. We evaluated the Mahalanobis distance using RGB values, indicative of color differences between cancerous and non-cancerous areas. We then conducted diagnosis test using a support vector machine (SVM) for each image. SVM was trained using the 100 training samples per class and determined which area each of 1900 test samples per class came from. Results The means of the Mahalanobis distances for WL, Indigo, and AIM were 1.52, 1.32, and 2.53, respectively and there were no significant differences in the three modalities. Diagnosability per endoscopy technique was assessed using the F1 measure. The means of F1 measures for WL, Indigo, and AIM were 0.636, 0.618, and 0.687, respectively. AIM images were better than WL and Indigo images for the diagnosis of GC. Conclusion Objective assessment by SVM found AIM to be suitable for diagnosis of early GC based on color differences.

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Section: Discussionmentioning

confidence: 99%

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Ogawa

Nishikawa

Hideura

et al. 2018

J Gastrointest Canc

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“…Panasyuk et al 17 utilized HSI to detect small residual tumors of 0.5 to 1 mm in a breast cancer surgery of a rodent tumor model. Kiyotoki et al 18 reported the use of HSI technology for the distinction of gastric cancer from normal mucosa in endoscopically resected lesions. Gebhart et al 19 used an imaging system that combined fluorescence and diffuse reflectance imaging for human brain tumor resection guidance.…”

Section: Introductionmentioning

confidence: 99%

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

Wang

Qin

et al. 2015

J. Biomed. Opt

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Abstract. Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450-to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

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“…Therefore, HSI finds applications in many applications, including the food industry [4,5], archeology [6], forensics [7,8], security [9], geology [1], [10][11][12], and medicine [13]. Clinical applications of HSI include retinal vascular inspection [14], early detection of gastric cancer [15], tongue lesion classification [16], and intraoperative tumors residuals detection [17].…”

Section: Introductionmentioning

confidence: 99%

High Throughput AOTF Hyperspectral Imager for Randomly Polarized Light

Abdlaty

Orepoulos²,

Sinclair³

et al. 2018

Photonics

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Abstract:The acousto-optic tunable filter (AOTF) is one of the most used techniques for hyperspectral imaging (HSI), and is capable of fast and random wavelength access, high diffraction efficiency, and good spectral resolution. Typical AOTF-HSI works with linearly polarized light; hence, its throughput is limited for randomly polarized applications such as fluorescence imaging. We report an AOTF-based imager design using both polarized components of the input light. The imager is designed to operate in the 450 to 800 nm region with resolutions in the range of 1.5-4 nm. The performance characterization results show that this design leads to 68% improvement in throughput for randomly polarized light. We also compared its performance against a liquid crystal tunable filter (LCTF)-based imager.

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New method for detection of gastric cancer by hyperspectral imaging: a pilot study

Cited by 102 publications

References 14 publications

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery

High Throughput AOTF Hyperspectral Imager for Randomly Polarized Light

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