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Epidermis area detection for immunofluorescence microscopy
Author(s): Andrey Dovganich; Andrey Krylov; Andrey Nasonov; Natalia Makhneva
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Paper 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.

Paper Details

Date Published: 10 April 2018
PDF: 5 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061522 (10 April 2018); doi: 10.1117/12.2302591
Show Author Affiliations
Andrey Dovganich, Lomonosov Moscow State Univ. (Russian Federation)
Andrey Krylov, Lomonosov Moscow State Univ. (Russian Federation)
Andrey Nasonov, Lomonosov Moscow State Univ. (Russian Federation)
Natalia Makhneva, MV Vladimirsky Moscow Regional Research and Clinical Institute (Russian Federation)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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