An Affordable, Portable Fluorescence Imaging Device for Skin Lesion Detection Using a Dual Wavelength Approach for Image Contrast Enhancement and Aminolaevulinic Acid-induced Protoporphyrin IX. Part II. In Vivo Testing

2001 ◽  
Vol 16 (3) ◽  
pp. 207-212 ◽  
Author(s):  
F. Fischer ◽  
E.F. Dickson ◽  
J.C. Kennedy ◽  
R.H. Pottier
Keyword(s):  
Skin Lesion ◽  
Contrast Enhancement ◽  
Fluorescence Imaging ◽  
Protoporphyrin Ix ◽  
Lesion Detection ◽  
Dual Wavelength ◽  
Imaging Device ◽  
Image Contrast Enhancement ◽  
In Vivo Testing

Method for In-Vivo Fluorescence Imaging Contrast Enhancement through Light Modulation

2016 ◽  
Vol 27 (1) ◽  
pp. 13-20
Author(s):  
Jaeyoung Kim ◽  
Onseok Lee ◽  
Seunghan Ha ◽  
Jung Woo Lee ◽  
Chilhwan Oh
Keyword(s):  
Contrast Enhancement ◽  
Fluorescence Imaging ◽  
Light Modulation ◽  
In Vivo Fluorescence ◽  
In Vivo Fluorescence Imaging

scholarly journals Detection of bacterial fluorescence from in vivo wound biofilms using a point‐of‐care fluorescence imaging device

2021 ◽  
Author(s):  
Andrea J. Lopez ◽  
Laura M. Jones ◽  
Landrye Reynolds ◽  
Rachel C. Diaz ◽  
Isaiah K. George ◽  
...  
Keyword(s):  
Fluorescence Imaging ◽  
Point Of Care ◽  
Imaging Device ◽  
Wound Biofilms

scholarly journals A Ratiometric Fluorescence Imaging System for Surgical Guidance

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Eduardo H. Moriyama ◽  
Anthony Kim ◽  
Arjen Bogaards ◽  
Lothar Lilge ◽  
Brian C. Wilson
Keyword(s):  
Fluorescence Imaging ◽  
Aminolevulinic Acid ◽  
Imaging System ◽  
Protoporphyrin Ix ◽  
Surgical Guidance ◽  
Ccd Imaging ◽  
Imaging Geometry ◽  
Fluorescence Imaging System ◽  
In Vivo Fluorescence Imaging

A 3-chip CCD imaging system has been developed for quantitative in vivo fluorescence imaging. This incorporates a ratiometric algorithm to correct for the effects of tissue optical absorption and scattering, imaging “geometry” and tissue autofluorescence background. The performance was characterized, and the algorithm was validated in tissue-simulating optical phantoms for quantitative measurement of the fluorescent molecule protoporphyrin IX (PpIX). The technical feasibility to use this system for fluorescence-guided surgical resection of malignant brain tumor tissue was assessed in an animal model in which PpIX was induced exogenously in the tumor cells by systemic administration of aminolevulinic acid (ALA).

scholarly journals Spectrally tunable light source based on a digital micromirror device for retinal image contrast enhancement

2015 ◽  
Vol 55 (3) ◽  
Author(s):  
Piotr Bartczak ◽  
Daiga Čerāne ◽  
Pauli Fält ◽  
Pasi Ylitepsa ◽  
Elina Hietanen ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Light Source ◽  
Retinal Image ◽  
Image Contrast ◽  
Image Feature ◽  
Optical Methods ◽  
Digital Micromirror Device ◽  
Artificial Eye ◽  
Image Contrast Enhancement

Different optical methods for retinal imaging provide a significant improvement for image analysis and help with data interpretation. The use of tunable light sources, which have been optimized for contrast enhancement of various retinal features or lesions in retinal images, could simplify the eye fundus examination through enhanced image quality. In this study, we have developed and described a research prototype which consists of a spectrally tunable light source based on a digital micromirror device which is further coupled to a fundus camera. The overall aim of this construction was to generate illuminations optimized for enhanced retinal image feature visibility. The optimized illumination conditions were compared to traditional red-free imaging and the measurements were executed for an artificial eye followed by in vivo measurements of the eyes of three volunteers. In all cases, the retinal image contrast was observed to improve compared to the traditional red-free imaging. Depending on the observed retinal feature, the perceptual improvements in the contrast varied from a few percent to nearly 70 percent.

scholarly journals An Efficient multi-level Pre-processing Algorithm for the Enhancement of Dermoscopy images in Melanoma detection

2021 ◽  
Author(s):  
JEBA DERWIN D ◽  
JEBA SINGH O ◽  
PRIESTLY SHAN B
Keyword(s):  
Skin Lesion ◽  
Contrast Enhancement ◽  
Extreme Learning Machine ◽  
Positive Impact ◽  
Lesion Detection ◽  
Phase Congruency ◽  
Lesion Segmentation ◽  
Multi Level ◽  
Learning Machine ◽  
Structural Preservation

Abstract In this paper, a multi-level algorithm for Pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal and virtual shaving before the skin lesion segmentation. The NLM filter with lowest BRISQUE score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the de-noised image. RICE algorithm is used for contrast enhancement, produces enhanced images with better structural preservation and negligible loss of information. Unsharp Masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase-congruency based method in virtual shaving shows high similarity with groundtruth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework shows that, the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information and elimination of artifacts in melanoma images. Output of proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results it is found that, the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

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