Instituto de Informática, Universidade Federal do Rio Grande do Sul. Avenida Bento Gonçalves 9500, Porto Alegre 91501-970, RS, Brazil.
Comput Methods Programs Biomed. 2013 Dec;112(3):684-93. doi: 10.1016/j.cmpb.2013.08.010. Epub 2013 Aug 27.
Melanoma is a type of malignant melanocytic skin lesion, and it is among the most life threatening existing cancers if not treated at an early stage. Computer-aided prescreening systems for melanocytic skin lesions is a recent trend to detect malignant melanocytic skin lesions in their early stages, and lesion segmentation is an important initial processing step. A good definition of the lesion area and its border is very important for discriminating between benign and malignant cases. In this paper, we propose to segment melanocytic skin lesions using a sequence of steps. We start by pre-segmenting the skin lesion, creating a new image representation (channel) where the lesion features are more evident. This new channel is thresholded, and the lesion border pre-detection is refined using an active-contours algorithm followed by morphological operations. Our experimental results based on a publicly available dataset suggest that our method potentially can be more accurate than comparable state-of-the-art methods proposed in literature.
黑素瘤是一种恶性黑色素瘤皮肤病变，如果在早期得不到治疗，它是最具威胁生命的癌症之一。用于黑色素瘤皮肤病变的计算机辅助预筛选系统是一种早期检测恶性黑色素瘤皮肤病变的新趋势，而病变分割是一个重要的初始处理步骤。良好的病变区域及其边界定义对于区分良性和恶性病例非常重要。在本文中，我们提出了一种使用一系列步骤来分割黑色素瘤皮肤病变的方法。我们首先对皮肤病变进行预分割，创建一个新的图像表示（通道），其中病变特征更为明显。这个新的通道被阈值化，然后使用主动轮廓算法对病变边界的预检测进行细化，接着进行形态学操作。我们基于一个公开可用的数据集的实验结果表明，与文献中提出的可比的最先进方法相比，我们的方法可能更准确。
