Microstructure preserving synthesis of biomedical images

We present an approach for synthesizing biological tissue textures from existing tissue samples. At microscopic resolution, tissues are characterized by a spatial arrangement of nuclei, cytoplasm, red-blood cells (RBCs) and adipose components etc. We employ 2-point correlation functions (2-pcfs) to encode the geometrical aspects of component arrangements in the synthesis process. The 2-pcfs belong to a class of neighborhood density estimators and were recently introduced in microscopic image analysis. We provide examples of their application toward synthesis of histology-based tissue textures. We show that our methods retain properties such as component volume fractions, sizes and density in comparison to standard approaches. Our methods are also shown to improve the performance of segmentation algorithms by automatically generating labeled texture classes.