IEEE J Biomed Health Inform. 2017 Nov;21(6):1685-1693. doi: 10.1109/JBHI.2017.2653179. Epub 2017 Jan 16.
The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.
皮肤病变的分割是自动计算机辅助黑色素瘤诊断的基本步骤。然而，当病变边界不清晰且病变与周围皮肤之间的对比度低时，传统的分割方法会遇到困难。当存在异质背景或病变触及图像边界时，它们的性能也会很差，这会导致皮肤病变的分割不足或过度。我们建议使用从稀疏表示模型得出的重构误差进行显着性检测，并结合新颖的背景检测，可以更准确地将病变与周围区域区分开来。我们进一步提出了一个贝叶斯框架，可以更好地描绘病变的形状和边界。我们还在两个包含 1100 张皮肤镜图像的公共数据集上评估了我们的方法，并将其与其他传统和最先进的无监督（即无需培训）病变分割方法以及最先进的无监督显着性检测方法进行了比较。我们的结果表明，与其他方法相比，我们的方法在分割病变方面更加准确和鲁棒。我们还讨论了我们的框架作为病变分割的显着性优化算法的一般扩展。
