Multispectral device for help in diagnosis (original) (raw)
Related papers
Applied Optics, 2018
Optical spectroscopy can be used to distinguish between healthy and diseased tissue. In this study, the design and testing of a single-pixel hyperspectral imaging (HSI) system that uses autofluorescence emission from collagen (400 nm) and nicotinamide adenine dinucleotide phosphate (475 nm) along with differences in the optical reflectance spectra to differentiate between healthy and thermally damaged tissue is discussed. The changes in protein autofluorescence and reflectance due to thermal damage are studied in ex vivo porcine tissue models. Thermal lesions were created in porcine skin (n 12) and liver (n 15) samples using an IR laser. The damaged regions were clearly visible in the hyperspectral images. Sizes of the thermally damaged regions as measured via HSI are compared to sizes of these regions as measured in white-light images and via physical measurement. Good agreement between the sizes measured in the hyperspectral images, white-light imaging, and physical measurements were found. The HSI system can differentiate between healthy and damaged tissue. Possible applications of this imaging system include determination of tumor margins during surgery/biopsy and cancer diagnosis and staging.
HYPERSPECTRAL IMAGING TECHNIQUE AS A STATE OF ART TECHNOLOGY IN MEAT SCIENCE
Nowadays, the concern of meat consumption, safety and quality has been popular due to some health risks such coronary heart disease, stroke and diabetes caused by the content as saturated fat, cholesterol content and carcinogenic compounds, for consumers. The importance of the need of new non-destructive and fast meat analyze methods are increasing day by day. For this, researchers have developed some methods to objectively measure the meat quality and meat safety as well as illness sources. Hyperspectral imaging technique is one of the most popular technology which combines imaging and spectroscopic technology. This technique is a non-destructive, real-time and easy-to-use detection tool for meat quality and safety assessment. It is possible to determine chemical structure and related physical properties of meat. It is clear that hyperspectral imaging technology can be automated for manufacturing in meat industry and all of data's obtained from the hyperspectral images which represents the chemical quality parameters of meats in the process can be saved to database.
Sensors
Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400–900 nm) and short-wave infrared (900–1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was hig...
Computerized Medical Imaging and Graphics, 2005
In this study, the digital transformation (digital staining) of the 16-band multispectral image of a hematoxylin and eosin (HE) stained pathological specimen to its Masson's trichrome (MT) stained counterpart is addressed. The digital staining procedure involves the classification of the various H&E-stained tissue components and then the transformation of their transmittance spectra to their equivalent MT-stained transmittance configurations. Combination of transmittance classifiers were designed to classify the various tissue components found in the multispectral images of an HE-stained specimen, e.g. nucleus, cytoplasm, red blood cell (RBC), fibrosis, etc.; while pseudoinverse method was used to obtain the transformation matrices that would translate the transmittance spectra of the classified HE-stained multispectral pixels to their MT-stained configurations. To generate the digitally stained image, weighting factors, which were based on the classifiers beliefs, were introduced to the generated transformation matrices. Initial results of our experiments on liver specimens show the viability of multispectral imaging (MSI) to implement a digital staining framework in the pathological context.
A Survey on Multispectral Imaging: Applications for Medical Diagnostics
2018
Hyperspectral imaging plays a vital role in the medical field. Major success of a surgeons work depends on his or her ability to identify problems of patients and cure them, particularly those that were not anticipated. Thus, this problem was solved by the invention of hyperspectral imaging modalities. Hyperspectral imaging techniques, along with associated algorithms and image processing methodologies have been developed by the military for detecting, classifying and identifying targets amid background clutter. Applying this technology to medicine will solve most of the problems associated with anatomy and identification of diseases. 1 International Journal of Pure and Applied Mathematics Volume 120 No. 6 2018, 721-726 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/
Optical characterization and imaging of biological tissues
The biological tissues exhibit their distinct optical characteristics. Their point-to-point compositional variation could either be determined by the optical parameters or reconstruction of the reflectance or tomographic images. The optical parameters, absorption and reduced scattering coefficients of goat tissues and human post-mastectomy breast specimen are determined by matching of their reflectance profile, as measured by multi-probe laser reflectometer, with that obtained by Monte Carlo simulation of optical scattering. The reflectance image of the mastectomy sample is reconstructed by measurement of reflectance at its various locations. The image thus obtained shows point-to-point variation in tissue composition. By multi-slice tomographic system the size and shape of the inclusions of different optical properties in a phantom made of goat fat are determined. By these procedures the structural variation in healthy and diseased tissues are determined and their relevance to early detection of tumour in tissues is discussed.
Journal of Clinical Medicine, 2019
Hyperspectral/Multispectral imaging (HSI/MSI) technologies are able to sample from tens to hundreds of spectral channels within the electromagnetic spectrum, exceeding the capabilities of human vision. These spectral techniques are based on the principle that every material has a different response (reflection and absorption) to different wavelengths. Thereby, this technology facilitates the discrimination between different materials. HSI has demonstrated good discrimination capabilities for materials in fields, for instance, remote sensing, pollution monitoring, field surveillance, food quality, agriculture, astronomy, geological mapping, and currently, also in medicine. HSI technology allows tissue observation beyond the limitations of the human eye. Moreover, many researchers are using HSI as a new diagnosis tool to analyze optical properties of tissue. Recently, HSI has shown good performance in identifying human diseases in a non-invasive manner. In this paper, we show the pote...
HYPERSPECTRAL IMAGING (HSI): APPLICATIONS IN ANIMAL AND DAIRY SECTOR
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 seafoods. 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.
Hyperspectral characterization of tissue in the SWIR spectral range: a road to new insight?
Optical Biopsy XVII: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2019
Hyperspectral imaging is a generic imaging modality allowing high spectral and spatial resolution over a wide wavelength range from the visible to mid-infrared. Short wavelength infrared (SWIR) hyperspectral imaging is currently becoming an important supplement to spectroscopy in optical diagnostics due to the flexibility and adaptability of the technique. However, due to the complexity of hyperspectral data, the analysis requires a well planned approach. In this paper a simple but effective approach combining dimension reduction and unsupervised classification is suggested. Examples of in vivo hyperspectral data in the SWIR spectral range (950-2500 nm) from human skin bruises and porcine skin burns are presented as examples. Data are processed using the minimum noise fraction transform (MNF), and K-means clustering. K-means clustering was found to perform significantly better if applied to MNF transformed data. The classification results agree well with biopsies, spectral data and visual inspection of injuries. It is thus shown that unsupervised clustering can be a preferable technique in cases where it is challenging to use or interpret results from physics based models, or where the ground truth is lacking or not well defined. The presented results confirm that SWIR hyperspectral imaging indeed is a useful tool for optical characterization of tissue.
Applied Sciences, 2021
Fat content is one of the most important parameters of beef grading. In this study, a hyperspectral imaging (HSI) system, combined with multivariate data analysis, was adopted for the classification of beef grades. Three types of beef samples, namely Akaushi (AK), USDA prime, and USDA choice, were used for HSI image acquisition in the spectral range of 400–1000 nm. Spectral information was extracted from the image by applying the partial least squares discriminant analysis (PLS-DA) for the three classifications. A total of eight different types of data pre-processing procedures were tested during PLS-DA to evaluate their individual performance, with the accepted pre-processing method selected based on the highest accuracy. Chemical and binary images were generated to visualize the fat mapping of the samples. Quantitative analysis of the samples was performed for the reference measurement of the dry matter and fat content. The highest overall accuracy, 86.5%, was found using the Savi...