Multispectral imaging (MSI) provides a sequence of en-face fundus spectral slices and allows for the examination of structures and signatures throughout the thickness of retina to characterize diabetic retinopathy (DR) lesions comprehensively. Manual interpretation of MSI images is commonly conducted by qualitatively analyzing both the spatial and spectral properties of multiple spectral slices. Meanwhile, there exist few computer-based algorithms that can effectively exploit the spatial and spectral information of MSI images for the diagnosis of DR. We propose a new approach that can quantify the spatial-spectral features of MSI retinal images for automatic DR lesion segmentation. It combines a generalized low-rank approximation of matrices with a supervised regularization term to generate low-dimensional spatial-spectral representations using the feature vectors in all spectral slices. Experimental results showed that the proposed approach is very effective for the segmentation of DR lesions in MSI images, which suggests it as an interesting tool for assisting ophthalmologists in diagnosing, analyzing, and managing DR lesions in MSI.

中文翻译：

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使用低维空间光谱矩阵表示法在多光谱图像中分割糖尿病性视网膜病变

多光谱成像（MSI）提供了一系列的眼底频谱切片，并允许检查整个视网膜厚度的结构和特征，以全面表征糖尿病性视网膜病变（DR）病变。通常通过定性分析多个光谱切片的空间和光谱特性来进行MSI图像的手动解释。同时，很少有基于计算机的算法可以有效地利用MSI图像的空间和光谱信息来诊断DR。我们提出了一种新方法，可以量化MSI视网膜图像的空间光谱特征，以进行自动DR病变分割。它结合了矩阵的广义低秩逼近和监督正则化项，以使用所有光谱切片中的特征向量生成低维空间光谱表示。实验结果表明，该方法对MSI图像中DR病变的分割非常有效，表明它是协助眼科医生诊断，分析和管理MSI中DR病变的有趣工具。