Detection of melanomas by digital imaging of spectrally resolved ultraviolet light-induced autofluorescence of human skin

Chwirot, Barbara W.; Chwirot, S.; Redzinski, Jaroslaw; Michniewicz, Zbigniew

doi:10.1016/s0959-8049(98)00210-x

Cited by 49 publications

(32 citation statements)

References 9 publications

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“…Using a neural network system, they noted alterations in protein and lipid structure in the BCC as exhibited in the Raman spectra compared to that of normal skin. Chwirot et al [26] used 366 nm excitation light from filtered xenon-Hg lamp and CCD camera equipped with a narrow band 475 nm filter to detect melanoma from other pigmented lesions. They developed an algorithm based on the signal intensities from lesion and surrounding normal tissues that could differentiate melanomas from dysplastic nevi and other pigmented lesions.…”

Section: Discussionmentioning

confidence: 99%

Laser‐induced fluorescence spectroscopy for in vivo diagnosis of non‐melanoma skin cancers

et al. 2002

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Section: Discussionmentioning

confidence: 99%

Laser‐induced fluorescence spectroscopy for in vivo diagnosis of non‐melanoma skin cancers

et al. 2002

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“…The development of automated systems for melanoma classification preoccupies many biomedical laboratories (22,23,27,29,31), which will be examined further. It is also interesting to include studies in our survey that discuss with the general problem of skin lesion image characterization, as they face similar problems.…”

Section: Systems In Literaturementioning

confidence: 99%

“…Computer-based systems for the characterization of digital skin images. (27)(28)(29). Microscopy or epiluminence microscopy installations have also been applied (23,31).…”

Section: Systems In Literaturementioning

confidence: 99%

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Computational vision systems for the detection of malignant melanoma

Kosmopoulos¹

2006

Oncol Rep

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Abstract. In recent years, computational vision-based diagnostic systems for dermatology have demonstrated significant progress. We review these systems by first presenting the installation, visual features utilized for skin lesion classification and the methods for defining them. We also describe how to extract these features through digital image processing methods, i.e. segmentation, registration, border detection, color and texture processing, and present how to use the extracted features for skin lesion classification by employing artificial intelligence methods, i.e. discriminant analysis, neural networks, and support vector machines. Finally, we compare these techniques in discriminating malignant melanoma tumors versus dysplastic naevi lesions.

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“…Steadystate fluorescence imaging has been utilized to a much lesser extent to detect epithelial precancers and cancers from relatively large tissue fields ( a few centimeters in diameter) within the upper aero-digestive tract, tracheo-bronchial tree, and gastrointestinal tissue [1]- [3]. Specifically, several groups have developed endoscopic-compatible [4]- [10] and nonendoscopicbased [11]- [13] fluorescence-imaging systems for these applications. These systems are based on multipixel illumination and detection.…”

Section: Introductionmentioning

confidence: 99%

Fast and noninvasive fluorescence imaging of biological tissues in vivo using a flying-spot scanner

Ramanujam

Chen

Gossage

et al. 2001

IEEE Trans. Biomed. Eng.

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Abstract-We have developed a flying-spot scanner (FSS), for fluorescence imaging of tissues in vivo. The FSS is based on the principles of single-pixel illumination and detection via a raster scanning technique. The principal components of the scanner are a laser light source, a pair of horizontal and vertical scanning mirrors to deflect the laser light in these respective directions on the tissue surface, and a photo multiplier tube (PMT) detector. This paper characterizes the performance of the FSS for fluorescence imaging of tissues in vivo. First, a signal-to-noise ratio (SNR) analysis is presented. This is followed by characterization of the experimental SNR, linearity and spatial resolution of the FSS. Finally, the feasibility of tissue fluorescence imaging is demonstrated using an animal model. In summary, the performance of the FSS is comparable to that of fluorescence-imaging systems based on multipixel illumination and detection. The primary advantage of the FSS is the order-of-magnitude reduction in the cost of the light source and detector. However, the primary disadvantage of the FSS its significantly slower frame rate (1 Hz). In applications where high frame rates are not critical, the FSS will represent a low-cost alternative to multichannel fluorescence imaging-systems.

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Detection of melanomas by digital imaging of spectrally resolved ultraviolet light-induced autofluorescence of human skin

Cited by 49 publications

References 9 publications

Laser‐induced fluorescence spectroscopy for in vivo diagnosis of non‐melanoma skin cancers

Laser‐induced fluorescence spectroscopy for in vivo diagnosis of non‐melanoma skin cancers

Computational vision systems for the detection of malignant melanoma

Fast and noninvasive fluorescence imaging of biological tissues in vivo using a flying-spot scanner

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