Development of an Advanced Hyperspectral Imaging (HSI) System with Applications for Cancer Detection

Martin, Matthew; Wabuyele, Musundi B.; Chen, Kui; Kasili, Paul M.; Panjehpour, Masoud; Phan, Mary N.; Overholt, Bergein F.; Cunningham, Glenn; Wilson, Dagmar C.; DeNovo, Robert C.; Vo‐Dinh, Tuan

doi:10.1007/s10439-006-9121-9

Cited by 177 publications

(116 citation statements)

References 23 publications

(17 reference statements)

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“…The optical image with both spectral and spatial information can be obtained in one measurement and does not require contact between the object and the sensor, allowing spectral libraries to be built in order to assign the registered spectra to specific components, at the same time identified both as quality and quantity in the sample. The usefulness of such technique in biological field was demonstrated, such as for example in the characterization of single biomolecules [13,21], in the cytological diagnosis of cancerous tissues [3,15,20], as well as in the spectral characterization of gold nanoparticle distribution and pathogens in the blood [18,19]. Some of us reported an initial approach of HDFM analysis of blood cells, evidencing the possibility to work with whole blood, without need of fluorescent markers, and envisaging that the membrane cell morphology and types influenced the spectral characteristics [25].…”

Section: Introductionmentioning

confidence: 99%

Building up spectral libraries for mapping erythrocytes by hyperspectral dark field microscopy

Conti

Scanferlato

Louka³

et al. 2016

BSI

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Abstract. BACKGROUND:Red blood cells (RBC) are obtained by non-invasive methods and widely used for diagnostic tests of health status. Hyperspectral Dark Field Microscopy (HDFM) is a promising technique for nanoscale bio imaging and spectral analysis without additional sample preparation. OBJECTIVE: Develop a protocol for human RBC characterization by HDFM, checking the feasibility of a reference spectral library that can image and afford a new comprehensive descriptor of RBC status. METHOD: A step-by-step protocol for HDFM measurement of human RBC was for the first time established using 5 µl of EDTA-treated whole blood from healthy adults (n = 30). Hyperspectral characteristics of solutions/suspensions at biological concentrations of phospholipids, hemoglobin, spectrin, cholesterol and protoporphyrin IX, as the most relevant RBC components, were also determined. RESULTS: A library made of 8 end-member spectra and classification of their spectral distribution carried out by Single Angle Mapper (SAM) were determined, furnishing a comprehensive mapping and descriptor of healthy human RBC. The spectra of single components allowed some of the RBC spectral bands to be attributed. CONCLUSIONS: This work reports for the first time the hyperspectral optical imaging of the human RBC by a library made of 8 scattering spectra, whose spectral signatures are compared with those of the main RBC molecular components. The percent distribution of the spectral end-members was also achieved, thus giving for the first time the HDFM mapping of human healthy RBCs. The protocol developed herein allows the clinical potential of hyperspectral imaging to be developed for the use of RBC mapping in health and disease.

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Section: Introductionmentioning

confidence: 99%

Building up spectral libraries for mapping erythrocytes by hyperspectral dark field microscopy

Conti

Scanferlato

Louka³

et al. 2016

BSI

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“…In diabetic patients, HSI has been found to be useful for prediction and follow-up of foot ulcers for healing (Khaodhiar et al, 2007). An important application of HIS in medicine is discrimination of cancerous tissue from healthy tissue (Martin et al, 2006).Thus, apart from determining quality and safety attributes of livestock products, the technique can also be used for non-destructive diagnosis of diseases such as cancers (Siddiqi et al, 2008;Akbari et al, 2011;Liu et al, 2012).…”

Section: Applications Of Hsi In Dairy Industrymentioning

confidence: 99%

Hyperspectral imaging (hsi): applications in animal and dairy sector

Kumar¹,

Saxena²,

Shrivastava³

et al. 2016

JEBAS

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Hyperspectral imaging (HSI), also known as imaging spectroscopy or 3D spectroscopy, combines imaging and spectroscopy into a single system. With a high resolution measurement of spectral signatures, HSI is able to provide critical information of the target. Thus it is useful for various scientific and industrial applications, including food safety and disease diagnosis. Due to constantly increasing demands for safe animal products, there is pressure on the processing sector for applications of advanced, high throughput methods for non-destructive quality analysis of animal products. In this context, HSI finds its applications for grading, classification, quality & composition analysis of animal products including meat, egg, milk etc. Further, the technique is also a useful tool in poultry sector for assessment of wholesomeness and quality control of chicken carcasses, as well as, chicken meat products. In fish industry also, the technique has established its potential for determining freshness and quality attributes of sea-foods. Apart from quality control of animal products, HSI has also demonstrated its usefulness for disease diagnosis in animal models and for detection of mammary cancers in dogs. Thus, the future of HSI technology in animal industry is promising and associated with multivariate analysis, HSI technique will further dominate in animal products authentication and analysis in the future also.

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“…Hyperspectral imaging has been used to discriminate between cancerous and normal tissue [7]. However, this method is limited to the visible wavelength range and requires the injection of fluorescent material.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral imaging and spectral-spatial classification for cancer detection

Fei

Akbari

Halig

2012

2012 5th International Conference on BioMedical Engineering and Informatics

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Abstract-Hyperspectral imaging is an emerging modality for medical applications. Its spectroscopic data can be utilized to noninvasively detect cancer. In this study, an advanced image processing and classification method is proposed to analyze hyperspectral image data for prostate cancer detection. Least squares support vector machines (LS-SVMs) were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. The method was used to detect prostate cancer in tumor-bearing mice. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice show that the hyperspectral imaging and classification method was able to reliably detect prostate tumors in the animal model. This study may lead to advances in the optical diagnosis of cancer using hyperspectral imaging.

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Development of an Advanced Hyperspectral Imaging (HSI) System with Applications for Cancer Detection

Cited by 177 publications

References 23 publications

Building up spectral libraries for mapping erythrocytes by hyperspectral dark field microscopy

Building up spectral libraries for mapping erythrocytes by hyperspectral dark field microscopy

Hyperspectral imaging (hsi): applications in animal and dairy sector

Hyperspectral imaging and spectral-spatial classification for cancer detection

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