Cheng Yue, Swamisai Ragavendar, Umbaugh Scott E, Moss Randy H, Stoecker William V, Teegala Saritha, Srinivasan Subhashini K
Electrical and Computer Engineering Department, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1801, USA.
Skin Res Technol. 2008 Feb;14(1):53-64. doi: 10.1111/j.1600-0846.2007.00261.x.
BACKGROUND/PURPOSE: Clinically, it is difficult to differentiate the early stage of malignant melanoma and certain benign skin lesions due to similarity in appearance. Our research uses image analysis of clinical skin images and relative color-based pattern recognition techniques to enhance the images and improve differentiation of these lesions.
First, the relative color images were created from digitized photographic images. Then, they were segmented into objects and morphologically filtered. Next, the relative color features were extracted from the objects to form two different feature spaces; a lesion feature space and an object feature space. The two feature spaces serve as two data models to be analyzed to determine the best features. Finally, we used a statistical analysis model of relative color features, which better classifies the various types of skin lesions.
RESULTS/CONCLUSIONS: The best features found for differentiation of melanoma and benign skin lesions from this study are area, mean in the red and blue bands, standard deviation in the red and green bands, skewness in the green band, and entropy in the red band. With the relative color feature algorithm developed, the best results were obtained with a multi-layer perceptron neural network model. This showed an overall classification success of 79%, with 70% of the benign lesions successfully classified, and 86% of malignant melanoma successfully classified.
背景/目的：在临床上，由于外观相似，很难区分恶性黑色素瘤的早期阶段和某些良性皮肤病变。我们的研究使用临床皮肤图像的图像分析和基于相对颜色的模式识别技术来增强图像并改善这些病变的区分。
首先，从数字化摄影图像创建相对颜色图像。然后，将它们分割成对象并进行形态学滤波。接下来，从对象中提取相对颜色特征以形成两个不同的特征空间；病变特征空间和对象特征空间。这两个特征空间用作两个要分析的数据模型，以确定最佳特征。最后，我们使用了相对颜色特征的统计分析模型，该模型能更好地对各种类型的皮肤病变进行分类。
结果/结论：本研究中发现的区分黑色素瘤和良性皮肤病变的最佳特征是面积、红色和蓝色波段的平均值、红色和绿色波段的标准差、绿色波段的偏度以及红色波段的熵。使用开发的相对颜色特征算法，通过多层感知器神经网络模型获得了最佳结果。这显示总体分类成功率为79%，其中70%的良性病变成功分类，86%的恶性黑色素瘤成功分类。
