Detection of pigment network in dermatoscopy images using texture analysis
Detection of pigment network in dermatoscopy images using texture analysis
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DOI：
10.1016/j.compmedimag.2004.04.002
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发表时间：
2004-07-01
影响因子：
5.7
通讯作者：
Stoecker, WV
中科院分区：
文献类型：
--
作者：
Anantha, M;Moss, RH;Stoecker, WV
皮肤镜检查，也称为皮肤镜检查或表皮发光显微镜检查 (ELM)，是一种非侵入性体内技术，可以使肉眼检查无法辨别的色素沉着黑素细胞肿瘤的特征可视化。 ELM 提供了一系列全新的视觉功能。其中一个显着特征是颜料网络。开发了两种基于纹理的算法用于色素网络的检测。这些方法适用于皮肤镜图像中的各种纹理图案，包括缺乏细纹的图案，例如鹅卵石、毛囊或加粗的网络图案。两种纹理算法（Laws 能量掩模和邻域灰度相关矩阵 (NGLDM) 大量强调）在一组 155 个皮肤镜检查图像上进行了优化并进行了比较。结果表明，Laws 能量掩模在皮肤镜图像中色素网络检测方面具有优越性。对于这两种方法，皮肤镜检查图像的纹素宽度为 10 像素或大约 0.22 毫米。 (C) 2004 年，爱思唯尔有限公司出版。
Dermatoscopy, also known as dermoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such prominent feature is the pigment network. Two texture-based algorithms are developed for the detection of pigment network. These methods are applicable to various texture patterns in dermatoscopy images, including patterns that lack fine lines such as cobblestone, follicular, or thickened network patterns. Two texture algorithms, Laws energy masks and the neighborhood gray-level dependence matrix (NGLDM) large number emphasis, were optimized on a set of 155 dermatoscopy images and compared. Results suggest superiority of Laws energy masks for pigment network detection in dermatoscopy images. For both methods, a texel width of 10 pixels or approximately 0.22 mm is found for dermatoscopy images. (C) 2004 Published by Elsevier Ltd.