We present an end-to-end system for the automatic measurement of flow-mediated dilation (FMD) and intima-media thickness (IMT) for the assessment of the arterial function.
The video sequences are acquired from a B-mode echographic
scanner. A spline model (deformable template) is fitted to the
data to detect the artery boundaries and track them all along
the video sequence. The a priori knowledge about the image
features and its content is exploited. Preprocessing is performed
to improve both the visual quality of video frames for visual
inspection and the performance of the segmentation algorithm
without affecting the accuracy of the measurements. The system
allows real-time processing as well as a high level of interactivity
with the user. This is obtained by a graphical user interface
(GUI) enabling the cardiologist to supervise the whole process
and to eventually reset the contour extraction at any point in time.
The system was validated and the accuracy, reproducibility, and
repeatability of the measurements were assessed with extensive
in vivo experiments. Jointly with the user friendliness, low cost,
and robustness, this makes the system suitable for both research
and daily clinical use.
In this paper the authors analyze how the description and presentation of results about an algorithm proposed in the literature should be modified in order to comply with the Reproducible Signal Processing paradigm. We describe the problems one is faced with, by specifically focusing on how the description of the algorithm should be improved with respect to the classical approach.
In this paper, we propose a fully automatic system for analyzing ecographic movies of flow-mediated dilation. Our approach uses a spline-based active contour (deformable template) to follow artery boundaries during the FMD procedure. A number of preprocessing steps (grayscale conversion, contrast enhancing, sharpening) are used to improve the visual quality of frames coming from the echographic acquisition. Our system can be used in real-time environments due to the high speed of edge recognition which iteratively minimizes fitting errors on endothelium boundaries. We also implemented a fully functional GUI which permits to interactively follow the whole recognition process as well as to reshape the results. The system accuracy and reproducibility has been validated with extensive in vivo experiments.
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