Challenges of Automatic Processing of Large Amount of Skin Lesion Multispectral Data
Proceedings of SPIE. Vol.11585: Biophotonics - Riga 2020 2020
Ilze Lihacova, Evija Cibuļska, Alexey Lihachev, Marta Lanģe, Emilija-Vija Ploriņa, Dmitrijs Bļizņuks, Aleksandrs Derjabo, Norbert Kiss

This work will describe the challenges involved in setting up automatic processing for a large differentiated data set. In this study, a multispectral (skin diffuse reflection images using 526nm (green), 663nm (red), and 964nm (infrared) illumination and autofluorescence (AF) image using 405 nm excitation) data set with 756 lesions (3024 images) was processed. Previously, using MATLAB software, finding markers, correctly segmenting images with dark edges and image alignment were the main causes of the problems in automatic data processing. To improve automatic processing and eliminate the use of licensed software, the latter was substituted with the open source Python environment. For more precise segmentation of skin markers and skin lesions, as well for image alignment, the processing of artificial neural networks was utilized. The resulting processing method solves most of the issues of the MATLAB script. However, for even more accurate results, it is necessary to provide more accurate ground-truth segmentation masks and generate more input data to increase the training image database by using data augmentation.


Keywords
artificial neural networks, multispectral melanoma diagnostics, skin lesion segmentation
DOI
10.1117/12.2582049
Hyperlink
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11585/2582049/Challenges-of-automatic-processing-of-large-amount-of-skin-lesion/10.1117/12.2582049.short

Lihacova, I., Cibuļska, E., Lihachev, A., Lanģe, M., Ploriņa, E., Bļizņuks, D., Derjabo, A., Kiss, N. Challenges of Automatic Processing of Large Amount of Skin Lesion Multispectral Data. In: Proceedings of SPIE. Vol.11585: Biophotonics - Riga 2020, Latvia, Riga, 24-25 August, 2020. Bellingham, Washington: SPIE, 2020, Article number 115850C. ISBN 9781510639997. e-ISBN 9781510640009. ISSN 0277-786X. e-ISSN 1996-756X. Available from: doi:10.1117/12.2582049

Publication language
English (en)
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