Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation

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.