Gurden S P, Monteiro V F, Longo E, Ferreira M M C
Department of Chemistry, State University of Campinas (UNICAMP), 13084-862, Campinas-SP, Brazil.
J Microsc. 2004 Jul;215(Pt 1):13-23. doi: 10.1111/j.0022-2720.2004.01350.x.
The surface topography of human hair, as defined by the outer layer of cellular sheets, termed cuticles, largely determines the cosmetic properties of the hair. The condition of the cuticles is of great cosmetic importance, but also has the potential to aid diagnosis in the medical and forensic sciences. Atomic force microscopy (AFM) has been demonstrated to offer unique advantages for analysis of the hair surface, mainly due to the high image resolution and the ease of sample preparation. This article presents an algorithm for the automatic analysis of AFM images of human hair. The cuticular structure is characterized using a series of descriptors, such as step height, tilt angle and cuticle density, allowing quantitative analysis and comparison of different images. The usefulness of this approach is demonstrated by a classification study. Thirty-eight AFM images were measured, consisting of hair samples from (a) untreated and bleached hair samples, and (b) the root and distal ends of the hair fibre. The multivariate classification technique partial least squares discriminant analysis is used to test the ability of the algorithm to characterize the images according to the properties of the hair samples. Most of the images (86%) were found to be classified correctly.
人类头发的表面形貌，由称为角质层的细胞片外层所定义，在很大程度上决定了头发的外观特性。角质层的状况在美容方面非常重要，同时在医学和法医学领域也具有辅助诊断的潜力。原子力显微镜（AFM）已被证明在分析头发表面方面具有独特优势，这主要归功于其高图像分辨率和简便的样品制备方法。本文提出了一种用于自动分析人类头发AFM图像的算法。通过一系列描述符（如台阶高度、倾斜角度和角质层密度）来表征角质层结构，从而实现对不同图像的定量分析和比较。一项分类研究证明了该方法的有效性。共测量了38张AFM图像，这些图像包括来自（a）未处理和漂白头发样本以及（b）头发纤维根部和末端的头发样本。采用多元分类技术偏最小二乘判别分析来测试该算法根据头发样本特性表征图像的能力。结果发现，大多数图像（86%）被正确分类。
