Abstract
The paper presents a method for an automatic folds detection in the whole slide images to support the pathomorphological diagnostic procedure. The studied slides represent the meningiomas and oligodendrogliomas tumour stained with the Ki-67/MIB-1 immunohistochemical reaction. The proposed method is based on texture analysis (local binary pattern and Unser), mathematical morphology and Support Vector Machine classification. The fold area detection is a necessary preprocessing step in the automatic examination of the histological specimens, such as hot-spot selection, quantitative evaluation etc. The results of the automatic fold detection were compared with the expert’s annotations. The achieved results confirm efficiency of the proposed solutions.
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Swiderska-Chadaj, Z., Markiewicz, T., Grala, B., Slodkowska, J. (2016). Local Binary Patterns and Unser Texture Descriptions to the Fold Detection on the Whole Slide Images of Meningiomas and Oligodendrogliomas. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_76
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DOI: https://doi.org/10.1007/978-3-319-32703-7_76
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