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An imaging device with 405 nm LED illumination at power density 7 mW/cm2 was used for cutaneous autofluorescence excitation. Autofluorescence photobleaching was detected by imaging under continuous irradiation for 20 seconds. It was found that on average basal cell carcinoma in patients with basal cell nevus syndrome has a lower autofluorescence intensity at the first second of excitation, as well as smaller decrease in intensity after 20 seconds of irradiation compared to sporadic basal cell carcinoma. This may show that basal cell carcinoma in patients with basal cell nevus syndrome have a different composition of endogenous fluorophores than in sporadic cases which could be investigated in further research.
System includes embedded processing module, CMOS camera, 670nm laser diode and optionally WiFi module for connecting to external image storage system. Due to small size, system could be fully placed in laboratory incubator with constant humidity and temperature. By using laser diode, Petri dish with microorganisms’ substrate is illuminated with speckle pattern. Embedded camera and processing system obtain images and stores them for processing with neural network.
Neural network utilizes “3D ConvNets” architecture with ability to encode not only spatial speckle variance, but also their changes in time. Convolutive approach allows significantly reduce the number of trained parameters, therefore reducing training and detection time. Neural network training used 200 bacteria colonies and additional 300 areas without bacteria. In the result, trained neural network reaches 0.95 accuracy score, that proves correctness of the approach.
This study aims at finding solutions of image quality problems in the area of biophotonics. The resulting image quality depends on hardware capabilities of the object illumination, image sensor, optical system and image post processing (image storage format). Although several of the quality problems of the imaging systems may be prevented in advance, some flaws may not be removed as easily. For example, uneven illumination cases, where skin is not flat (for example: nose, ear). Due to that, it is not possible to create uniform illumination field and the resulting optical image has noticeable differences across it. Sometimes, it is the skin texture that could cause problems for the automatic malformation classification and diagnosis. In this case, image quality enhancement can be helpful for removing different image flaws and raise the precision of malformation classification.
In this research methods for solving different image quality problems in multispectral images of skin malformations are proposed. Multispectral image acquisition and proposed methods are tested on noncontact skin cancer analyzing device prototype. Nevertheless, it could be applied on other multispectral image analysis algorithms. Pilot studies of filtering methods show good results when trying to deal with uneven lighting problems in images. Quality enhancement methods include high pass filtering, extraction of nonskin fragments (hair, markers, etc.), image stabilization and other methods. The image quality enhancement techniques were clinically tested on multispectral images of different skin malformations and the results of the study are presented in this paper.
Results of clinical approbation to assess the efficacy of the new device to diagnose malignant skin lesions will be demonstrated.
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