Kopriva I, Persin A
Department of Laser and Atomic Research and Development, Rudjer Bosković Institute, Zagreb, Croatia.
Med Image Anal. 2009 Jun;13(3):507-18. doi: 10.1016/j.media.2009.02.002. Epub 2009 Feb 20.
Unsupervised decomposition of static linear mixture model (SLMM) with ill-conditioned basis matrix and statistically dependent sources is considered. Such situation arises when low-dimensional low-intensity multi-spectral image of the tumour in the early stage of development is represented by the SLMM, wherein tumour is spectrally similar to the surrounding tissue. The original contribution of this paper is in proposing an algorithm for unsupervised decomposition of low-dimensional multi-spectral image for high-contrast tumour visualisation. It combines nonlinear band generation (NBG) and dependent component analysis (DCA) that itself combines linear pre-processing transform and independent component analysis (ICA). NBG is necessary to improve conditioning of the extended mixing matrix in the SLMM, while DCA is necessary to increase statistical independence between spectrally similar sources. We demonstrate good performance of the method on both computational model and experimental low-intensity red-green-blue fluorescent image of the surface tumour (basal cell carcinoma). We believe that presented method can be of use in other multi-channel medical imaging systems.
考虑对具有病态基矩阵和统计相关源的静态线性混合模型（SLMM）进行无监督分解。当用SLMM表示肿瘤发育早期的低维低强度多光谱图像时，就会出现这种情况，其中肿瘤在光谱上与周围组织相似。本文的原创贡献在于提出一种用于低维多光谱图像无监督分解以实现高对比度肿瘤可视化的算法。它结合了非线性波段生成（NBG）和相关成分分析（DCA），而DCA本身又结合了线性预处理变换和独立成分分析（ICA）。NBG对于改善SLMM中扩展混合矩阵的条件是必要的，而DCA对于增加光谱相似源之间的统计独立性是必要的。我们在计算模型和表面肿瘤（基底细胞癌）的实验低强度红绿蓝荧光图像上都证明了该方法的良好性能。我们相信所提出的方法可用于其他多通道医学成像系统。
