Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Kho, Esther; Dashtbozorg, Behdad; Boer, Lisanne L. de; Vijver, Koen K. Van de; Sterenborg, Henricus J. C. M.; Ruers, Theo J.M.

doi:10.1364/boe.10.004496

Cited by 50 publications

(65 citation statements)

References 35 publications

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“…Three experimental detections in Section 3 demonstrated the capacity of our MMHI system in the transmission, reflection and fluorescence modes, providing potential areas of application, such as zooplankton detection [4], biometric applications [24,25] and environmental monitoring [26]. These experiments also show the feasibility of the MMHI system to detect samples with different characteristics, such as the transmission mode for transparent/translucent samples, the reflection mode for opaque samples and the fluorescence mode for fluorescent samples.…”

Section: Discussionmentioning

confidence: 80%

Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples

Jiang

2020

Applied Sciences

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In this work, we develop a multi-mode microscopic hyperspectral imager (MMHI) for the detection of biological samples in transmission imaging, reflection imaging and fluorescence mode. A hyperspectral image cube can be obtained with 5 μm spatial resolution and 3 nm spectral resolution through push-broom line scanning. To avoid possible shadows produced by the high magnification objective with a short working distance, two illumination patterns are designed to ensure the co-axiality of the illumination and detection. Three experiments for the detection of zebrafish and fingerprints and the classification of disaster-causing microalgae verify the good capability and functionality of the system. Based on the detected spectra, we can observe the impacts of β-carotene and melanin in zebrafish, hemoglobin in the fingertip, and chlorophyll in microalgae, respectively. Multi-modes can be switched freely according to the application requirement and characteristics of different samples, like transmission mode for the transparent/translucent sample, reflection mode for the opaque sample and fluorescence mode for the fluorescent sample. The MMHI system also has strong potential for the non-invasive and high-speed sensing of bio or clinical samples.

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Section: Discussionmentioning

confidence: 80%

Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples

Jiang

2020

Applied Sciences

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“…The most commonly used method is the U-Net, as first used in HSI by Trajanovski et al for tongue cancer detection with a 2D input data using all HS channels for semantic segmentation of ex-vivo specimens [74]. Additionally, Kho et al used ex-vivo specimens from patients with breast cancer and applied a standard U-net with 2D input HS data using all spectral channels for semantic segmentation [75].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…In-vivo brain tumor detection ‡ [27] Deep learning 2D-CNN and 1D-DNN In-vivo brain tumor detection ‡ [69] 2D-CNN (Inception v4) Head and neck cancer [71] Salivary gland cancer [72] 2D-CNN and 3D-CNN Head and neck cancer [73] 2D-CNN (U-Net) Tongue cancer detection [74] Breast cancer [75] GAN HS image generation from RGB [76] RNNs, 2D-CNN and 3D-CNN Head and neck cancer detection [77] Publicly available datasets are marked with ‡ .…”

Section: Spatial and Spectral Features In Supervised Classificationmentioning

confidence: 99%

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Ortega¹,

Halicek²,

Fabelo³

et al. 2021

Multimedia Information Retrieval

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Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI information extraction techniques in clinical applications.

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“…Using SNV normalization, the mean of each spectrum was set to zero and the standard deviation was set to one. The SNV is often used for diffuse reflection HSI to exclude the influence of glare on the spectra [46,47]. However, SNV normalization also removes information on the scattering.…”

Section: Preprocessingmentioning

confidence: 99%

Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra

Baltussen

Sterenborg

Ruers

et al. 2019

Biomed. Opt. Express

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Diffuse reflectance spectroscopy can be used in colorectal cancer surgery for tissue classification. The main challenge in the classification task is to separate healthy colorectal wall from tumor tissue. In this study, four normalization techniques, four feature extraction methods and five classifiers are applied to nine datasets, to obtain the optimal method to separate spectra measured on healthy colorectal wall from spectra measured on tumor tissue. All results are compared to the use of the entire non-normalized spectra. It is found that the most optimal classification approach is to apply a feature extraction method on non-normalized spectra combined with support vector machine or neural network classifier.

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Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Cited by 50 publications

References 35 publications

Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples

Multi-mode Microscopic Hyperspectral Imager for the Sensing of Biological Samples

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra

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