10 April 2018 Epidermis area detection for immunofluorescence microscopy
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061522 (2018) https://doi.org/10.1117/12.2302591
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
We propose a novel image segmentation method for immunofluorescence microscopy images of skin tissue for the diagnosis of various skin diseases. The segmentation is based on machine learning algorithms. The feature vector is filled by three groups of features: statistical features, Laws’ texture energy measures and local binary patterns. The images are preprocessed for better learning. Different machine learning algorithms have been used and the best results have been obtained with random forest algorithm. We use the proposed method to detect the epidermis region as a part of pemphigus diagnosis system.
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Andrey Dovganich, Andrey Dovganich, Andrey Krylov, Andrey Krylov, Andrey Nasonov, Andrey Nasonov, Natalia Makhneva, Natalia Makhneva, } "Epidermis area detection for immunofluorescence microscopy", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061522 (10 April 2018); doi: 10.1117/12.2302591; https://doi.org/10.1117/12.2302591
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