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We propose a semi-automatic approach for aorta centerline extraction in contrast-enhanced MRI, making aorta length analysis feasible on large scale. Starting from user-specified start and end regions, we extract the aorta path in between the regions automatically. The extraction is formulated as an optimization problem, seeking for the path that most likely runs central to the aorta. To this end, we exploit that the aorta distinguishes from the surrounding by strong image gradients that point inwards to the aorta’s center due to contrast-enhanced imaging. We also include additional means of manual guidance to resolveerroneous cases. Experiments on data of 19 subjects yielded results that are close to the inter-reader variability. The average distance to the ground truth was 1.89 ± 1.54 mm, while aorta lengths deviated by only 0.66 ± 0.49 %.

We propose an automatic approach to aorta segmentation in axial cardiac cine MRI. The segmentation task is formulated as a prob- abilistic inference problem, seeking for the most probable constellation of aorta locations and shapes in time. To this end, a graphical model is developed that implements the mutual dependencies of the aorta param- eters along the cine sequence. Our approach integrates effective means of manual guidance for post-correction in case of erroneous results, re- quiring only user interaction where necessary. Experiments on a data set of 20 cine sequences showed average Dice coefficients close to the inter- reader variability while outperforming previous work in the field. Only two post-corrections were required for the entire data set. Results also indicate high stability of our approach w.r.t. re-parameterization.

We address the task of aortic diameter measurement in (noncontrast- enhanced) plain axial cardiac cine MRI. To this end, we set up a likelihood maximization problem which allows us to recover globally optimal aorta locations and diameters of the cine sequence efficiently. Our approach provides intuitive means of manual post-correction and requires little user interaction, making large-scale image analysis feasible. Experiments on a data set of 20 cine sequences with 30 time frames showed (at least) pixel-accurate diameter measurements which are also highly stable against re-parameterization.

Muscle ultrasonography is a convenient technique to visualize healthy and pathological muscle tissue as it is non-invasive and image acquisition can be done in real-time. In this paper, a texture-based approach is presented to detect myositis in ultrasound images automatically. We compute different texture features like wavelet transform features and first-order grey-level intensity statistics of a relevant central image patch carrying structure and intensity information of muscle tissue. Using a combination of these information we reached an accuracy of classification of 92.20 % with our approach on a training data set of 63 clinically pre-classified data sets.