Analysis of skin line pattern for lesion classification
Analysis of skin line pattern for lesion classification
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DOI：
10.1034/j.1600-0846.2003.00370.x
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发表时间：
2003-02-01
影响因子：
2.2
通讯作者：
Fish, PJ
中科院分区：
文献类型：
--
作者：
She, ZS;Fish, PJ
背景/目的：已经观察到，皮肤模式倾向于被恶性而非良性皮肤病变所破坏。这表明，在简单捕获的白光光学皮肤图像上测量皮肤模式破坏可能对诊断特征集有有用的贡献。先前的工作是通过一致的高值分析技术测量线强度，然后进行局部方差测量或区域聚集分类器来测量皮肤线模式破坏，这是非常有希望的，但计算量很大，这表明测量皮肤模式破坏的想法是有用的，但需要更简单的方法。方法：采用高通滤波提取皮肤图案，并采用自适应各向异性（空间变异）滤波增强，该滤波沿皮肤线条平滑，但不跨皮肤线条平滑。使用局部图像梯度矩阵估计皮肤线主方向和方向方差，并将这些测量值在病变图像边界上的差异作为病变分类器。结果：对一组恶性黑色素瘤和良性痣的图像进行了上述处理，结果在二维特征（线方向和线变异差）空间的散点图显示了良好的良恶性病灶分离。ROC图的面积为0.88。结论：实验结果表明，局部线方向和局部线变化是区分恶性黑色素瘤和良性病变的有希望的特征，所采用的方法有效且计算成本低。
Background/purpose: It has been observed that skin patterning tends to be disrupted by malignant but not by benign skin lesions. This suggests that measurements of skin pattern disruption on simply captured white light optical skin images could be a useful contribution to a diagnostic feature set. Previous work using a measurement of line strength by a consistent high-value profiling technique followed by local variance measurement or a region agglomerative classifier to measure skin line pattern disruption was extremely promising but computationally intensive, suggesting that the idea of measuring skin pattern disruption was useful but a simpler method was required.Methods: The skin pattern was extracted by high-pass filtration and enhanced by adaptive anisotropic (spatial variant) filtering which smoothes along skin lines but not across them. The skin line main direction and direction variance were estimated using a local image gradient matrix and the difference of these measures across the lesion image boundary was used as a lesion classifier.Results: A set of images of malignant melanoma and benign naevi were processed as above and the scatter plot of results in a two-dimensional feature (line direction and line variation difference) space showed excellent separation of benign and malignant lesions. An ROC plot enclosed an area of 0.88.Conclusions: The experimental results showed that the local line direction and the local line variation were promising features for distinguishing malignant melanoma from benign lesion and the methods used were effective and computationally low-cost.