Toward automated detection and segmentation of aortic calcifications from radiographs

This paper aims at automatically measuring the extent of calcified plaques in the lumbar aorta from standard radiographs. Calcifications in the abdominal aorta are an important predictor for future cardiovascular morbidity and mortality. Accurate and reproducible measurement of the amount of calcified deposit in the aorta is therefore of great value in disease diagnosis and prognosis, treatment planning, and the study of drug effects. We propose a two-step approach in which first the calcifications are detected by an iterative statistical pixel classification scheme combined with aorta shape model optimization. Subsequently, the detected calcified pixels are used as the initialization for an inpainting based segmentation. We present results on synthetic images from the inpainting based segmentation as well as results on several X-ray images based on the two-steps approach.