Queen's University Belfast, Belfast, BT3 9DT, United Kingdom.
Comput Med Imaging Graph. 2011 Mar;35(2):121-7. doi: 10.1016/j.compmedimag.2010.08.002. Epub 2010 Sep 15.
Image segmentation is an important task in the analysis of dermoscopy images since the extraction of skin lesion borders provides important cues for accurate diagnosis. In recent years, gradient vector flow based algorithms have demonstrated their merits in image segmentation. However, due to the compromise of internal and external energy forces within the partial differential equation these methods commonly lead to under- or over-segmentation problems. In this paper, we introduce a new mean shift based gradient vector flow (GVF) algorithm that drives the internal/external energies towards the correct direction. The proposed segmentation method incorporates a mean shift operation within the standard GVF cost function. Theoretical analysis proves that the proposed algorithm converges rapidly, while experimental results on a large set of diverse dermoscopy images demonstrate that the presented method accurately determines skin lesion borders in dermoscopy images.
图像分割是皮肤镜图像分析中的一项重要任务,因为提取皮肤病变边界为准确诊断提供了重要线索。近年来,基于梯度向量流的算法在图像分割中表现出了优势。然而,由于偏微分方程中内部和外部能量力的妥协,这些方法通常会导致欠分割或过分割问题。在本文中,我们引入了一种新的基于均值漂移的梯度向量流(GVF)算法,该算法将内部/外部能量引导到正确的方向。所提出的分割方法在标准 GVF 代价函数中包含了均值漂移操作。理论分析证明了所提出的算法具有快速收敛性,而在大量不同的皮肤镜图像上的实验结果表明,所提出的方法能够准确地确定皮肤镜图像中的皮肤病变边界。