A computational framework to characterize and compare the geometry of coronary networks

Summary This work presents a computational framework to perform a systematic and comprehensive assessment of the morphometry of coronary arteries from in vivo medical images. The methodology embraces image segmentation, arterial vessel representation, characterization and comparison, data storage, and finally analysis. Validation is performed using a sample of 48 patients. Data mining of morphometric information of several coronary arteries is presented. Results agree to medical reports in terms of basic geometric and anatomical variables. Concerning geometric descriptors, inter‐artery and intra‐artery correlations are studied. Data reported here can be useful for the construction and setup of blood flow models of the coronary circulation. Finally, as an application example, similarity criterion to assess vasculature likelihood based on geometric features is presented and used to test geometric similarity among sibling patients. Results indicate that likelihood, measured through geometric descriptors, is stronger between siblings compared with non‐relative patients. Copyright © 2016 John Wiley & Sons, Ltd. We describe a computational framework to perform a systematic and comprehensive assessment of the morphometry of coronary arteries from in vivo images. Data mining of morphometric information of several arteries is presented for a sample of 48 patients. Concerning geometric descriptors, inter‐artery and intra‐artery correlations are studied. Similarity criterion to assess vasculature likelihood based on geometric features is presented and used to test similarity among sibling patients. Results indicate that likelihood, as defined here, is stronger between siblings compared with non‐relatives.