Fuzzy logic techniques for blotch feature evaluation in dermoscopy images.
Fuzzy logic techniques for blotch feature evaluation in dermoscopy images.
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DOI：
10.1016/j.compmedimag.2008.10.001
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发表时间：
2009-01
影响因子：
5.7
通讯作者：
Cognetta, Armand B.
中科院分区：
文献类型：
--
作者：
Khan, Azmath;Gupta, Kapil;Stanley, R. J.;Stoecker, William V.;Moss, Randy H.;Argenziano, Giuseppe;Soyer, H. Peter;Rabinovitz, Harold S.;Cognetta, Armand B.
斑点，也称为无结构区域，对于皮肤镜皮肤病变图像中区分恶性黑色素瘤和良性病变至关重要。在本文中，研究了模糊逻辑技术用于自动检测斑点特征以区分恶性黑色素瘤。代表斑点大小以及相对和绝对斑点颜色的四个模糊集用于从一组皮肤镜检查皮肤病变图像中提取斑点区域。根据提取的斑点以及四个新特征计算了先前报告的五个斑点特征。使用神经网络分类器，在可能的 alpha 切割范围内优化恶性黑色素瘤鉴别结果，并与使用清晰斑点特征的结果进行比较。使用基于三个平面相对颜色和斑点大小的模糊逻辑技术从斑点计算出的特征产生了 81.2% 的最高诊断准确性。
Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.
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影响因子：
13.8
作者：
Argenziano, G;Soyer, HP;Wolff, K
通讯作者：
Wolff, K
影响因子：
7.7
作者：
Lee, T;Ng, V;McLean, D
通讯作者：
McLean, D
影响因子：
5.7
作者：
Stanley, RJ;Moss, RH;Aggarwal, C
通讯作者：
Aggarwal, C
影响因子：
3.4
作者：
Pellacani, G;Grana, C;Seidenari, S
通讯作者：
Seidenari, S
影响因子：
2.2
作者：
Stoecker, WV;Gupta, K;Shrestha, B
通讯作者：
Shrestha, B