Recently, the hair loss population, alopecia areata patients, is increasing due to various unconfirmed reasons such as environmental pollution and irregular eating habits. In this paper, we introduce an algorithm for preventing hair loss and scalp self-diagnosis by extracting HLF (hair loss feature) based on the scalp image using a microscope that can be mounted on a smart device. We extract the HLF by combining a scalp image taken from the microscope using grid line selection and eigenvalue. First, we preprocess the photographed scalp images using image processing to adjust the contrast of microscopy input and minimize the light reflection. Second, HLF is extracted through each distinct algorithm to determine the progress degree of hair loss based on the preprocessed scalp image. We define HLF as the number of hair, hair follicles, and thickness of hair that integrate broken hairs, short vellus hairs, and tapering hairs.

中文翻译：

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脱发的纤毛镜检查：使用网格线选择和特征值的脱发特征提取和计算

最近，由于各种未确认的原因，例如环境污染和不规律的饮食习惯，脱发人群，斑秃患者正在增加。在本文中，我们介绍了一种通过使用可安装在智能设备上的显微镜基于头皮图像提取HLF（脱发特征）来防止脱发和头皮自我诊断的算法。我们通过使用网格线选择和特征值结合从显微镜拍摄的头皮图像来提取HLF。首先，我们使用图像处理对拍摄的头皮图像进行预处理，以调整显微镜输入的对比度并最大程度地减少光反射。其次，通过各种不同的算法提取HLF，根据预处理的头皮图像确定脱发的进展程度。我们将HLF定义为头发，毛囊，