Automated extraction and description of dark areas in surface microscopy melanocytic lesion images
Automated extraction and description of dark areas in surface microscopy melanocytic lesion images
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DOI：
10.1159/000075041
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发表时间：
2004-01-01
期刊：
影响因子：
3.4
通讯作者：
Seidenari, S
中科院分区：
文献类型：
--
作者：
Pellacani, G;Grana, C;Seidenari, S
背景：无论是在临床检查还是使用自动图像分析程序中，黑素细胞病变（ML）内的暗区识别对于黑色素瘤的诊断都非常重要。目的：比较两种不同的自动识别和描述MLs脱毛显微镜图像暗区的方法，并评价其诊断能力。方法：建立了两种自动提取“绝对”暗区（ADAs）和“相对”暗区（rda）的方法及其描述参数集，并对339张偏振光视频显微镜获得的MLs图像进行了测试。结果：两种方法在黑素瘤和痣的暗区分布上均有显著差异，可以很好地区分MLs （ADAs和rda的诊断准确率分别为74.6和71.2%）。结论：两种暗区自动识别方法均可用于黑色素瘤诊断，并可在图像分析程序中实现。版权所有(C) 2004 S. Karger AG，巴塞尔。
Background: Identification of dark areas inside a melanocytic lesion (ML) is of great importance for melanoma diagnosis, both during clinical examination and employing programs for automated image analysis. Objective: The aim of our study was to compare two different methods for the automated identification and description of dark areas in epiluminescence microscopy images of MLs and to evaluate their diagnostic capability. Methods: Two methods for the automated extraction of 'absolute' (ADAs) and 'relative' dark areas (RDAs) and a set of parameters for their description were developed and tested on 339 images of MLs acquired by means of a polarized-light videomicroscope. Results: Significant differences in dark area distribution between melanomas and nevi were observed employing both methods, permitting a good discrimination of MLs (diagnostic accuracy = 74.6 and 71.2% for ADAs and RDAs, respectively). Conclusions: Both methods for the automated identification of dark areas are useful for melanoma diagnosis and can be implemented in programs for image analysis. Copyright (C) 2004 S. Karger AG, Basel.