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This paper presents a new method for the automatic segmentation of the common carotid artery in B-mode images. This method uses the instantaneous coefficient of variation edge detector, fuzzy classification of edges and dynamic programming. Several discriminating features of the intima and adventitia boundaries are considered, like the edge strength, the intensity gradient orientation, the valley shaped intensity profile and contextual information of the region delimited by those boundaries. The adopted fuzzy classification of edges helps avoiding low-pass filtering. The method is suited to real-time processing and user interaction is not required. Both the near and far wall boundaries can be detected in arteries with plaques of different types and sizes. Both expert manual and automatic tracings are significantly better for the far wall, due to the better visibility of the intima and adventitia boundaries. The automatic detection of the far wall shows an accuracy similar to the manual detections. For this wall, the error coefficient of variation for the mean intima-media thickness is in the range [5.6, 6.6 %] for automatic detections and in [6.7, 7.1 %] for manual ones. In the case of the near wall, the same coefficient of variation is in [11.2, 13.0 %] for automatic detections and in [5.9, 9.0 %] for manual detections. The mean intima-media thickness measurement errors observed for the far wall [Formula: see text] are among the best values reported for other fully automatic approaches. The application of this approach in clinical practice is encouraged by the results for the far wall and the short processing time (mean of 2.1 s per image).

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Feedback from patients gives the hope that Rehab@home can become a great tool for complementing their rehabilitation process. Implications for Rehabilitation Rehab@home can be used at home by patients with motor deficits, without the presence of a therapist, as a complement to conventional therapy for accelerating the rehabilitation process. The system provides exercises for improving the balance and the STS movement capabilities of patients, gives automatic feedback, and saves video and load information from the movements for future analysis by the therapist. Its most important feature is adaptability: the therapist is able to tune the difficulty of the exercises for adapting them to the needs of each patient. Patients get more engaged for this type of exercises and think they can take profit from using it.