Abstract.A longstanding issue in biomedical research concerns the biophysical factors affecting the color diversity of the human irides and its connection with ocular diseases. Although the pigmentation and morphological characteristics of heavily and moderately pigmented irides have been extensively discussed in the literature, similar studies are scarce for lightly pigmented irides. We present computer experiments indicating that the spectral signature of these specimens may be directly affected by still undetectable melanin distributions in the outermost iridal layers. Our findings represent in silico evidence of this relationship which, in turn, may have implications in the investigation of the higher risk of death from metastatic ocular melanoma verified in individuals with light-colored irides.

Recently, light interactions with organic matter have become the object of detailed investigations by image synthesis researchers. Besides allowing these materials to be rendered in a more intuitive manner, these efforts aim to extend the scope of computer graphics applications to areas such as applied optics and biomedical imaging. There are, however, organic materials that still lack predictive simulation solutions. Among these, the ocular tissues, especially those forming the human iris, pose the most challenging modeling problems which are often associated with data scarcity. In this paper, we describe the first biophysically-based light transport model for the human iris ever presented in the scientific literature. The proposed model algorithmically simulates the light scattering and absorption processes occurring within the iridal tissues, and computes the spectral radiometric responses of these tissues. Its design is based on the current scientific understanding of the iridal morphological and optical characteristics, and it is controlled by parameters directly related to these biophysical attributes. The accuracy and predictability of the spectral results provided by the model are evaluated through comparisons with actual measured iridal data, and its integration into rendering frameworks is illustrated through the generation of images depicting iridal chromatic variations.