Department of Biomedical Engineering and Physics, Shahid Beheshti University, Tehran, Iran.
Skin Res Technol. 2009 Nov;15(4):433-9. doi: 10.1111/j.1600-0846.2009.00383.x.
BACKGROUND/PURPOSE: During the recent years, many diagnostic methods have been proposed aiming at early detection of malignant melanoma. The texture of skin lesions is an important feature to differentiate melanoma from other types of lesions, and different techniques have been designed to quantify this feature. In this paper, we discuss a new approach based on independent component analysis (ICA) for extraction of texture features of skin lesions in clinical images.
After preprocessing and segmentation of the images, features that describe the texture of lesions and show high discriminative characteristics are extracted using ICA, and then these features, along with the color features of the lesions, are used to construct a classification module based on support vector machines for the recognition of malignant melanoma vs. benign nevus.
Experimental results showed that combining melanoma and nevus color features with proposed ICA-based texture features led to a classification accuracy of 88.7%.
ICA can be used as an effective tool for quantifying the texture of lesions.
背景/目的：近年来，已经提出了许多旨在早期检测恶性黑色素瘤的诊断方法。皮肤病变的纹理是将黑色素瘤与其他类型病变区分开来的重要特征，并且已经设计了不同的技术来量化此特征。在本文中，我们讨论了一种基于独立成分分析（ICA）的新方法，用于提取临床图像中皮肤病变的纹理特征。
对图像进行预处理和分割后，使用 ICA 提取描述病变纹理并具有高鉴别特征的特征，然后使用这些特征以及病变的颜色特征来构建基于支持向量机的分类模块，用于识别恶性黑色素瘤与良性痣。
实验结果表明，将黑色素瘤和痣的颜色特征与基于 ICA 的纹理特征相结合，可以达到 88.7％的分类准确性。
ICA 可用于定量病变的纹理。
