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Dermoscopy Image Segmentation Using a Modified Level Set Algorithm

机译:使用改进的水平集算法进行皮肤镜图像分割

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Border segmentation serves as the first step in the 'automated analysis of dermoscopy images for the purpose of diagnosing melanoma and other pigmented skin lesions. Increasing the efficiency of the level set method for the purpose of an appropriate segmentation is in direct relationship with initialization and estimating the level set controlling parameters. In this paper, using a modified level set algorithm, an inventive method for the segmentation of skin lesions is proposed. This algorithm can directly evolve from the initial segmentation by creating a weighted combination of different segmentation methods such as Fuzzy C-means (FCM) clustering, k-means clustering, Otsu and Iterative Methods. In addition, it will be possible to estimate the controlling parameters of the level set evolution from the obtained initial contour. The results of the proposed method and comparison with the labeled segmentation results by specialists have been obtained on a standard the dermoscopy image database.
机译:边界分割是“自动分析皮肤镜图像”的第一步,目的是诊断黑色素瘤和其他有色素的皮肤病变。为了适当分割的目的而提高水平设置方法的效率与初始化和估计水平设置控制参数直接相关。在本文中,使用改进的水平集算法,提出了一种新颖的皮肤病变分割方法。通过创建不同分割方法(例如模糊C均值(FCM)聚类,k均值聚类,Otsu和迭代方法)的加权组合,该算法可以直接从初始分割中演变而来。另外,有可能从所获得的初始轮廓估计水平集演变的控制参数。所提出的方法的结果以及与专家标记的分割结果的比较已在标准的皮肤镜图像数据库上获得。

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