Colour and contrast enhancement for improved skin lesion segmentation
Colour and contrast enhancement for improved skin lesion segmentation
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DOI：
10.1016/j.compmedimag.2010.08.004
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发表时间：
2011-03-01
影响因子：
5.7
通讯作者：
Iyatomi, Hitoshi
中科院分区：
文献类型：
--
作者：
Schaefer, Gerald;Rajab, Maher I.;Iyatomi, Hitoshi
准确提取病变边缘是分析皮肤镜皮肤病变图像的关键步骤。本文分析了皮肤镜图像分析中经常遇到的对比度差和缺乏颜色校准的问题。不同的光照或不同的设备会导致同一病变的不同图像颜色，从而给分割阶段带来困难。同样，低对比度使得精确的边界检测变得困难。我们提出了一种通过增强颜色信息和图像对比度的预处理步骤来提高病变分割算法性能的有效方法。我们将这一增强阶段与两种不同的分割算法相结合。一种技术依赖于通过迭代测量非病变像素来分析图像背景，而另一种技术利用协作神经网络进行边缘检测。在100张皮肤镜图像的数据集上进行了广泛的实验评估，这些图像具有从三位皮肤科专家那里获得的已知基本事实。结果表明，这两种技术都能提供良好的分割性能，并且通过与原始RGB图像的结果进行比较，证明了颜色增强步骤确实是至关重要的。(C)2010爱思唯尔有限公司。保留所有权利。
Accurate extraction of lesion borders is a critical step in analysing dermoscopic skin lesion images. In this paper, we consider the problems of poor contrast and lack of colour calibration which are often encountered when analysing dermoscopy images. Different illumination or different devices will lead to different image colours of the same lesion and hence to difficulties in the segmentation stage. Similarly, low contrast makes accurate border detection difficult. We present an effective approach to improve the performance of lesion segmentation algorithms through a pre-processing step that enhances colour information and image contrast. We combine this enhancement stage with two different segmentation algorithms. One technique relies on analysis of the image background by iterative measurements of non-lesion pixels, while the other technique utilises co-operative neural networks for edge detection. Extensive experimental evaluation is carried out on a dataset of 100 dermoscopy images with known ground truths obtained from three expert dermatologists. The results show that both techniques are capable of providing good segmentation performance and that the colour enhancement step is indeed crucial as demonstrated by comparison with results obtained from the original RGB images. (C) 2010 Elsevier Ltd. All rights reserved.