Texture analysis of tissue aging using global and cluster constrained local coding
Morphological age of various tissues provides important information about aging processes. In this paper, we propose a method for quantifying the tissue age of pharynx terminal bulb in C.elegans from microscopy images. Our method comprises two main components: (i) a multi-modal texture descriptor extracted at two scales; and (ii) an age quantification algorithm with global and cluster-constrained local coding. The performance of our proposed method is evaluated on 970 images of seven age classes in the publicly available IICBU dataset. Our method achieves 21% improvement in classification accuracy when compared to that of the benchmark platform, and our local coding algorithm outperformed the support vector machine model by 10%.