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Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image

机译:基于张量分解的特征彩色提取从临床彩色图像中的黑色素瘤的非侵入性诊断

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摘要

We propose a method for feature extraction from clinical color images, with application in classification of skin lesions. Proposed feature extraction method is based on tensor decomposition of the clinical color image of skin lesion. Since color image is naturally represented as a three- way tensor, it is reasonable to use multi-way techniques to capture the underlying information contained in the image. Extracted features are elements of the core tensor in the corresponding multi-way decomposition, and represent spatial- spectral profile of the lesion. In contrast to common methods that exploit either texture or spectral diversity of the tumor only, the proposed approach simultaneously captures spatial and spectral characteristics. The procedure is tested on a problem of noninvasive diagnosis of melanoma from the clinical color images of skin lesions, with overall sensitivity 82.1% and specificity 86.9%. Our method compares favorably with the state of the art results reported in the literature and provides an interesting alternative to the existing approaches.
机译:我们提出了一种从临床彩色图像中提取特征的方法,并应用于皮肤损伤的分类。提出的特征提取方法是基于皮肤病变的临床彩色图像的张量分解。由于彩色图像自然地表示为三向张量,因此使用多向技术来捕获图像中包含的基础信息是合理的。提取的特征是相应多方向分解中核心张量的元素,并表示病变的空间光谱图。与仅利用肿瘤的纹理或光谱多样性的常规方法相反,所提出的方法同时捕获了空间和光谱特征。从皮肤病变的临床彩色图像对黑色素瘤的非侵入性诊断问题进行了测试,总敏感性为82.1%,特异性为86.9%。我们的方法与文献中报道的最新技术水平相比具有优势,并为现有方法提供了有趣的替代方法。

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