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首页> 外文会议>2013 16th International Multi Topic Conference >Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method
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Automated segmentation of skin lesions: Modified Fuzzy C mean thresholding based level set method

机译：皮肤病变的自动分割：基于模糊C均值阈值的改进水平集方法

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摘要

Accurate segmentation of skin lesion can play a vital role in early detection of skin cancer. Taking the complexity and varieties of skin lesion images into consideration, we propose a new algorithm that combines the advantages of clustering, thresholding and active contour methods currently being used independently for segmentation purposes. A modified Fuzzy C mean thresholding algorithm is applied to initialize level set automatically and also for estimating controlling parameters for level set evolution. The performance of level set segmentation is subject to appropriate initialization, so the proposed initialization method is compared to some other state of the art initialization methods present in literature. The work has been tested on a clinical database of 238 images. Parameters for performance evaluation are presented in detail. Increased true detection rate and reduced false positive and false negative errors confirm the effectiveness of the proposed method for skin cancer detection.
机译：皮肤病变的准确分割在皮肤癌的早期检测中起着至关重要的作用。考虑到皮肤病变图像的复杂性和多样性，我们提出了一种新算法，该算法结合了目前用于分割目的的聚类，阈值和主动轮廓方法的优势。改进的Fuzzy C均值阈值算法被应用到自动初始化水平集以及估计水平集演化的控制参数。水平集分割的性能需要进行适当的初始化，因此将建议的初始化方法与文献中介绍的其他一些现有技术初始化方法进行了比较。该作品已在238张图像的临床数据库上进行了测试。详细介绍了性能评估的参数。真实检测率的提高和假阳性和假阴性错误的减少证实了所提出的皮肤癌检测方法的有效性。

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