Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images
Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images
复制标题
DOI：
10.1111/j.1600-0846.2010.00494.x
复制
发表时间：
2011-08-01
影响因子：
2.2
通讯作者：
Gomez, David D.
中科院分区：
文献类型：
--
作者：
Cheng, Beibei;Erdos, David;Gomez, David D.
背景资料：毛细血管扩张，即皮肤表面附近直径不同的小血管扩张，是用于检测基底细胞癌（BCC）的关键皮肤镜结构。区分th278这些血管从其他毛细血管扩张，这是常见的在阳光损伤的皮肤，是challenging.Methods：图像分析技术进行了研究，以找到血管结构BCC自动。血管的主屏幕使用优化的局部颜色下降技术。噪声过滤器的开发，以消除假阳性结构，主要是气泡，头发，斑点和溃疡边缘。从包含候选血管样结构的毛细血管扩张掩模中，计算形状、大小和归一化计数特征，以便于区分良性皮肤病变和具有毛细血管扩张的BCC。实验结果诊断准确率高达96.7%使用神经网络分类器，用于59个BCC和152个良性病变的数据集，用于基于从毛细血管扩张面具。结论：在目前的临床实践中，通过皮肤镜检查可以发现比临床检查更小的BCC。虽然几乎所有这些小BCC都有毛细血管扩张，但它们可以短而薄。长度和面积的归一化有助于检测这些较小的BCC。
Background: Telangiectasia, dilated blood vessels near the surface of the skin of small, varying diameter, are critical dermoscopy structures used in the detection of basal cell carcinoma (BCC). Distinguishing th278ese vessels from other telangiectasia, that are commonly found in sun-damaged skin, is challenging.Methods: Image analysis techniques are investigated to find vessels structures in BCC automatically. The primary screen for vessels uses an optimized local color drop technique. A noise filter is developed to eliminate false-positive structures, primarily bubbles, hair, and blotch and ulcer edges. From the telangiectasia mask containing candidate vessel-like structures, shape, size and normalized count features are computed to facilitate the discrimination of benign skin lesions from BCCs with telangiectasia.Results: Experimental results yielded a diagnostic accuracy as high as 96.7% using a neural network classifier for a data set of 59 BCCs and 152 benign lesions for skin lesion discrimination based on features computed from the telangiectasia masks.Conclusion: In current clinical practice, it is possible to find smaller BCCs by dermoscopy than by clinical inspection. Although almost all of these small BCCs have telangiectasia, they can be short and thin. Normalization of lengths and areas helps to detect these smaller BCCs.