Fluorescence and reflectance monitoring of human cervical tissue in vivo: a case study

Gustafsson, Ulf; McLaughlin, William E.; Jacobson, E.; Hakansson, Johan; Troy, P.; DeWeert, Michael J.; Pålsson, Sara; Thompson, Marcelo Soto; Svanberg, Sune; Vaitkuvienė, Aurelija; Svanberg, Katarina

doi:10.1117/12.479495

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…Figure 3a), and ventral tongue (VT) (Figure 4a) indicates alterations in both the epithelium and stroma. The contributing factors to this reduction in fluorescence intensity are the breakdown of collagen crosslinks in the stroma, thickening of the epithelium, increased epithelial scattering, loss of keratin in the epithelium, and increased hemoglobin absorption associated with increased microvascular density throughout the epithelial-stromal region [1,18,19]. The redshift of the AF emission at 500 nm observed during tissue transformation towards malignancy is associated with the loss of fluorophores that emit at shorter wavelengths, such as NADH and FAD and the increase in relative contribution from porphyrins.…”

Section: Discussionmentioning

confidence: 99%

Comparative evaluation of the diagnostic performance of autofluorescence and diffuse reflectance in oral cancer detection: a clinical study

Jayanthi

Subhash

Stephen

et al. 2011

Journal of Biophotonics

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Autofluorescence (AF) and diffuse reflectance (DR) spectroscopic techniques have shown good diagnostic accuracies for noninvasive detection of oral cavity cancer. In the present study, AF and DR spectra recorded in vivo from the same set of sites in 65 patients were analyzed using Principal component analysis (PCA) and linear discriminant analysis (LDA). The effectiveness of these two techniques was assessed by comparison with gold standard and their discrimination efficiency was determined from the area under the receiver operator characteristic (AUC-ROC) curve. Analysis using a DR technique shows a higher AUC-ROC of 0.991 as against 0.987 for AF spectral data.

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Section: Discussionmentioning

confidence: 99%

Comparative evaluation of the diagnostic performance of autofluorescence and diffuse reflectance in oral cancer detection: a clinical study

Jayanthi

Subhash

Stephen

et al. 2011

Journal of Biophotonics

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“…The primary optical methods for recognition of molecules are infrared (IR) absorption, Raman scattering, luminescence and backscattering reflectance (including time-resolved) spectroscopies, as well as imaging. Different approaches are used to select types of devices for clinical trials, from the point of monitoring [11] to hyperspectral imaging [12].…”

Section: Introductionmentioning

confidence: 99%

Optical spectroscopic methods for diagnostics of intervertebral disc degeneration

Varanius¹,

Čiplys²,

Gėgžna³

et al. 2018

physics

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The level of degeneration (LOD) in the human intervertebral disc (IVD) could determine the choice of patient treatment strategy, thus there is a need for methods applicable at a point of care that enable quick medical decisions. In this paper, infrared light absorption, FT-Raman scattering and fluorescence spectroscopy were used to analyse the same specimens from different groups of IVD samples. Samples from the lumbar part of spine of 37 patients (mean age 48.2 years old) were included in the study. The gender distribution was 27 women and 10 men. The distribution by LOD in the Pfirrmann scale was as follows: grade 3 (40.5%), grade 4 (48.6%) and grade 5 (10.8%) were evaluated in our samples. Spectroscopic signals were measured and analysed. Features such as medical condition (LOD), patient’s age and gender were taken into account. All three methods of spectroscopy revealed age-dependent spectroscopic features related to LOD. For the first time a promissing possibility was found to recognize the LOD by fluorescence spectra in human IVD material taking gender and age into account. The findings create a background for the design of equipment and the design of experiment of more advanced clinical trials. FT-IR absorption and FT-Raman scattering spectra demonstrated the age dependences of a few components. The Raman peak at 2707 cm–1 seems promising for recognition of lumbar IVD LOD, but a higher number of specimens is necessary.

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“…We conducted a technology exploration project for the development of a CAD system for colposcopy, prototyping image processing algorithms for glare removal, anatomic features detection, acetowhite region detection, lesion margin shape analysis, and blood vessel mosaic and punctuation structure detection using RGB images from 111 human subjects participating in a clinical study of our Hyperspectral Diagnostic Imaging (HSDI TM ) instrument 10,11 . Here, we present the details and preliminary results of the glare removal algorithm for single RGB color images of the cervix that can be used as a pre-processing step in CAD systems.…”

Section: Introductionmentioning

confidence: 99%

Automatic glare removal in reflectance imagery of the uterine cervix

Lange¹

2005

SPIE Proceedings

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Colposcopy is a diagnostic method used to detect cancer precursors and cancer of the uterine cervix. Computer-AidedDiagnosis (CAD) for colposcopy is a new field in medical image processing. Colposcopists analyze glare (glint or specular reflection) patterns on the cervix to assess the surface contour (3D topology) of lesions, an important feature used to evaluate lesion severity. However, glare in the imagery presents major problems for automated image analysis systems. Glare eliminates all information in affected pixels and can introduce artifacts in feature extraction algorithms, such as acetowhite region detection. Although cross-polarization filters can be used to eliminate glare, the reality is that we have to deal with glare when we want to use existing cervical image databases or use an instrument that does not provide cross-polarized imagery. Here, we present the details and preliminary results of a glare removal algorithm for RGB color images of the cervix that can be used as a pre-processing step in CAD systems. The algorithm can be extended to multispectral and hyperspectral imagery. The basic approach of the algorithm is to extract a feature image from the RGB image that provides a good glare to background ratio, to detect the glare regions in the feature image, to extend the glare regions to cover all pixels that have been affected by the glare, and to remove the glare in the affected regions by filling in an estimate of the underlying image features. In our current implementation we use the green (G) image component as the feature image, given its high glare to background ratio and simplicity of calculation. Glare regions are either detected as saturated regions or small high contrasted bright regions. Saturated regions are detected using an adaptive thresholding method. Small high contrasted bright regions are detected using morphological top hat filters with different sizes and thresholds. The full extent of the glare regions is estimated by using a morphological constraint watershed segmentation to find the contour of the glare regions and adding a constant dilatation. The image features are estimated by interpolating the R,G,B color components individually from the surrounding regions based on Laplace's equation and modifying the intensity (I) component of the HSI color space transformed image. As the glare pattern is important to the physician, we embed it as a just visible intensity texture that does not affect the image processing. The performance of the algorithm is demonstrated using human subject data.

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Fluorescence and reflectance monitoring of human cervical tissue in vivo: a case study

Cited by 11 publications

References 9 publications

Comparative evaluation of the diagnostic performance of autofluorescence and diffuse reflectance in oral cancer detection: a clinical study

Comparative evaluation of the diagnostic performance of autofluorescence and diffuse reflectance in oral cancer detection: a clinical study

Optical spectroscopic methods for diagnostics of intervertebral disc degeneration

Automatic glare removal in reflectance imagery of the uterine cervix

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