A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images
A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images
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DOI：
10.3390/sym10040119
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发表时间：
2018-04-01
期刊：
影响因子：
2.7
通讯作者：
Smarandache, Florentin
中科院分区：
文献类型：
--
作者：
Guo, Yanhui;Ashour, Amira S.;Smarandache, Florentin
本文提出了一种新的皮肤病变检测的基础上，神经网络聚类和自适应区域生长算法应用于皮肤镜图像，称为NCARG。首先，皮肤镜图像被映射到一个自适应集域使用剪切波变换结果的图像。图像通过三种成员关系来描述：真、不确定和假成员关系。然后在图像集合中定义一个不确定滤波器，以减少图像的不确定性。一个自适应的c-均值聚类算法被应用于皮肤镜图像的分割。根据聚类结果，采用自适应区域生长方法精确识别皮肤病变。为了评估该算法的性能，使用公共数据集（ISIC 2017）来训练和测试所提出的方法。随机选择50张图像用于训练，500张图像用于测试。几个指标进行测量，定量评估NCARG的性能。结果表明，所提出的方法具有检测病变的能力，具有较高的准确性，95.3%的平均值，相比，所获得的平均准确性，80.6%，发现当采用神经相似性评分和水平集（NSSLS）分割方法。
This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.