Deshabhoina Srinivas V, Umbaugh Scott E, Stoecker William V, Moss Randy H, Srinivasan Subhashini K
Department of Electrical and Computer Engineering, Southern Illinois University at Edwardsville, IL 62026, USA.
Skin Res Technol. 2003 Nov;9(4):348-56. doi: 10.1034/j.1600-0846.2003.00044.x.
To explore texture features in two-dimensional images to differentiate seborrheic keratosis from melanoma.
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.
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.
探索二维图像中的纹理特征，以区分脂溢性角化病和黑色素瘤。
描述了一种对皮肤肿瘤进行一致分类的系统方法。基于二阶直方图的纹理特征被用于识别能够持续区分恶性皮肤肿瘤（黑色素瘤）和良性皮肤肿瘤（脂溢性角化病）的特征或特征组合。271张皮肤肿瘤图像被分为训练集和测试集，以评估准确性和一致性。应用自动归纳法生成分类规则。使用数据分析和建模工具进一步深入了解特征空间。
总体而言，85% - 90%的脂溢性角化病图像能够与恶性皮肤肿瘤正确区分。发现特征相关性平均值、相关性范围、纹理能量平均值和纹理能量范围是区分脂溢性角化病和黑色素瘤的最重要特征。总体而言，通过纹理特征识别脂溢性角化病图像比识别黑色素瘤图像效果更好。
