Melanoma and seborrheic keratosis differentiation using texture features
Melanoma and seborrheic keratosis differentiation using texture features
复制标题
DOI：
10.1034/j.1600-0846.2003.00044.x
复制
发表时间：
2003-11-01
影响因子：
2.2
通讯作者：
Srinivasan, SK
中科院分区：
文献类型：
--
作者：
Deshabhoina, SV;Umbaugh, SE;Srinivasan, SK
目的：探讨脂溢性角化病和黑色素瘤的二维图像纹理特征，以鉴别脂溢性角化病和黑色素瘤。基于二阶直方图的纹理特征被用来识别能够一致地区分恶性皮肤肿瘤(黑色素瘤)和良性皮肤肿瘤(脂溢性角化病)的特征或特征的组合。为了保证准确性和一致性，将271幅皮肤肿瘤图像分为训练集和测试集。采用自动归纳法生成分类规则。结果与结论：85-90%的脂溢性角化病图像与恶性皮肤肿瘤有正确的鉴别。相关平均、相关极差、纹理能量平均和纹理能量极差是鉴别脂溢性角化病和黑色素瘤的最重要的特征。总体而言，脂溢性角化病图像通过纹理特征比黑色素瘤图像更好地识别。
Purpose: To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.Methods: A systematic approach to consistent classification of skin tumors is described. Texture features, based on the second-order histogram, were used to identify the features or a combination of features that could consistently differentiate a malignant skin tumor (melanoma) from a benign one (seborrheic keratosis). Two hundred and seventy-one skin tumor images were separated into training and test sets for accuracy and consistency. Automatic induction was applied to generate classification rules. Data analysis and modeling tools were used to gain further insight into the feature space.Result and Conclusions: In all, 85-90% of seborrheic keratosis images were correctly differentiated from the malignant skin tumors. The features correlation_average, correlation_range, texture_energy_average and texture_energy_range were found to be the most important features in differentiating seborrheic keratosis from melanoma. Over-all, the seborrheic keratosis images were better identified by the texture features than the melanoma images.