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Epidermis Area Detection for Immunofluorescence Microscopy

机译:表皮面积检测的免疫荧光显微镜

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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.
机译:我们提出了一种用于皮肤组织的免疫荧光显微镜图像的新型图像分割方法,用于诊断各种皮肤疾病。分割基于机器学习算法。特征向量由三组特征填充:统计特征,Laws的纹理能量度量和局部二进制模式。图像经过预处理以更好地学习。使用了不同的机器学习算法,并通过随机森林算法获得了最佳结果。我们使用提出的方法来检测表皮区域,作为天疱疮诊断系统的一部分。

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