Detection of granularity in dermoscopy images of malignant melanoma using color and texture features.
Detection of granularity in dermoscopy images of malignant melanoma using color and texture features.
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DOI：
10.1016/j.compmedimag.2010.09.005
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发表时间：
2011-03
影响因子：
5.7
通讯作者：
Drugge, Rhett
中科院分区：
文献类型：
--
作者：
Stoecker, William V.;Wronkiewiecz, Mark;Chowdhury, Raeed;Stanley, R. Joe;Xu, Jin;Bangert, Austin;Shrestha, Bijaya;Calcara, David A.;Rabinovitz, Harold S.;Oliviero, Margaret;Ahmed, Fatimah;Perry, Lindall A.;Drugge, Rhett
颗粒性，也称为胡椒粉样改变和多发性蓝灰色小点，在皮肤镜图像中被定义为微小蓝灰色颗粒的聚集。颗粒性与恶性黑色素瘤的诊断密切相关。本研究通过颜色和纹理测量分析颗粒区域，以区分黑色素瘤中的颗粒性与非黑色素瘤皮肤病变中的类似区域。
对74例黑色素瘤和14例原位黑色素瘤的皮肤镜图像中的颗粒区域进行了识别和手动选取。对于200张非黑色素瘤皮肤镜图像，也类似地选取了在颜色和纹理上与颗粒性最相似的区域。研究了10种纹理测量指标和22种颜色测量指标。纹理测量指标包括能量、惯性、相关性、反差分和熵的平均值和范围。颜色测量指标包括绝对和相对的RGB平均值、绝对和相对的RGB色度平均值、绝对和相对的G/B平均值、CIE X、Y、Z、X/Y、X/Z和Y/Z平均值、R方差以及亮度。对黑色素瘤的每个颗粒区域以及非黑色素瘤图像中的可比区域计算了这些指标。
受试者工作特征（ROC）曲线分析表明，通过颜色和纹理测量指标相结合，利用颗粒区域特征对黑色素瘤图像和非黑色素瘤图像的区分效果最佳。ROC结果比较显示，使用相对颜色比使用绝对颜色能更好地区分黑色素瘤和良性病变。统计分析表明，黑色素瘤中颗粒性的四个最显著指标是在斑点上平均的两种颜色指标和两种纹理指标：相对蓝色、相对绿色、纹理相关性和纹理能量范围。利用纹理和相对颜色测量指标的最佳特征集，根据受试者工作特征曲线下面积计算，准确率达到96.4%。
Granularity, also called peppering and multiple blue-gray dots, is defined as an accumulation of tiny, blue-gray granules in dermoscopy images. Granularity is most closely associated with a diagnosis of malignant melanoma. This study analyzes areas of granularity with color and texture measures to discriminate granularity in melanoma from similar areas in non-melanoma skin lesions. The granular areas in dermoscopy images of 74 melanomas and 14 melanomas in situ were identified and manually selected. For 200 non-melanoma dermoscopy images, those areas which most closely resembled granularity in color and texture were similarly selected. Ten texture and twenty-two color measures were studied. The texture measures consisted of the average and range of energy, inertia, correlation, inverse difference, and entropy. The color measures consisted of absolute and relative RGB averages, absolute and relative RGB chromaticity averages, absolute and relative G/B averages, CIE X, Y, Z, X/Y, X/Z and Y/Z averages, R variance, and luminance. These measures were calculated for each granular area of the melanomas and the comparable areas in the non-melanoma images. Receiver operating characteristic (ROC) curve analysis showed that the best separation of melanoma images from non-melanoma images by granular area features was obtained with a combination of color and texture measures. Comparison of ROC results showed greater separation of melanoma from benign lesions using relative color than using absolute color. Statistical analysis showed that the four most significant measures of granularity in melanoma are two color measures and two texture measures averaged over the spots: relative blue, relative green, texture correlation, and texture energy range. The best feature set, utilizing texture and relative color measures, achieved an accuracy of 96.4% based on area under the receiver operating characteristic curve.
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