Department of Computer Science, Loughborough University, Loughborough, UK.
Comput Med Imaging Graph. 2011 Mar;35(2):99-104. doi: 10.1016/j.compmedimag.2010.08.004. Epub 2010 Oct 28.
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.
准确提取病变边界是分析皮肤镜下皮肤病变图像的关键步骤。在本文中，我们考虑了在分析皮肤镜图像时经常遇到的对比度差和缺乏色彩校准的问题。不同的照明或不同的设备会导致同一病变的图像颜色不同，从而给分割阶段带来困难。同样，低对比度使得准确的边界检测变得困难。我们提出了一种有效的方法，通过增强色彩信息和图像对比度的预处理步骤来提高病变分割算法的性能。我们将这个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对非病变像素的迭代测量来分析图像背景，而另一种技术则利用合作神经网络进行边缘检测。我们在一个包含 100 张皮肤镜图像的数据集上进行了广泛的实验评估，这些图像的ground truth 是由三位专家皮肤科医生提供的。结果表明，这两种技术都能够提供良好的分割性能，并且色彩增强步骤确实是至关重要的，这可以通过与原始 RGB 图像的结果进行比较来证明。
