Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images
Thresholding methods for lesion segmentation of basal cell carcinoma in dermoscopy images
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DOI：
10.1111/srt.12352
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发表时间：
2017-08-01
影响因子：
2.2
通讯作者：
Stoecker, W. V.
中科院分区：
文献类型：
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作者：
Kaur, R.;LeAnder, R.;Stoecker, W. V.
目的：用于色素性病变分割的算法在基底细胞癌（BCC）（最常见的皮肤癌）的皮肤镜图像上表现不佳。主要目的是开发更好的方法BCC segmentation.Methods：15阈值方法实现BCC病变分割。我们提出了两个更好地衡量第二类错误的错误度量：相对XOR错误和病变捕获率。结果：在305和34 BCC图像的训练/测试集上，基于新的错误度量，五种新技术的性能优于两种用于黑色素瘤分割的最先进方法。所提出的算法包括图像渐晕校正和边界扩展的解决方案，以实现皮肤科医生般的边界，提供了更具包容性和特征保留的边界检测，有利于更好的BCC分类精度，在未来的工作。
Purpose: Algorithms employed for pigmented lesion segmentation perform poorly on dermoscopy images of basal cell carcinoma (BCC), the most common skin cancer. The main objective was to develop better methods for BCC segmentation.Methods: Fifteen thresholding methods were implemented for BCC lesion segmentation. We propose two error metrics that better measure the type II error: Relative XOR Error and Lesion Capture Ratio.Results: On training/test sets of 305 and 34 BCC images, respectively, five new techniques outperform two state-of-the-art methods used in segmentation of melanomas, based on the new error metrics.Conclusion: The proposed algorithms, which include solutions for image vignetting correction and border expansion to achieve dermatologist-like borders, provide more inclusive and feature-preserving border detection, favoring better BCC classification accuracy, in future work.