Motion-compensated frame rate up-conversion in carotid ultrasound images using optical flow and manifold learning.

OBJECTIVE: Carotid ultrasonography is a reliable and non-invasive method to evaluate atherosclerosis disease and its complications. B-mode cineloops are widely used to assess the severity of atherosclerosis and its progression; ho- wever, tracking rapid wall motions of the carotid artery is still a challenging issue due the low frame rate. The aim of this paper was to present a new hybrid frame rate up-conversion (FRUC) method that accounts for motion based on manifold learning and optical flow. METHODS: In the last decade, manifold learning technique has been used to pseudo-increase the frame rate of carotid ultrasound images, but due to the dependence of this method to the number of recorded cardiac cycles and frames, a new hybrid method based on manifold learning and optical flow was proposed in this paper. RESULTS: Locally linear embedding (LLE) algorithm was first applied to find the relation between the frames of consecutive cardiac cycles in a low dimensional manifold. Then by applying the optical flow motion estimation algorithm, a motion compensated frame was reconstructed. CONCLUSION: Consequently, a cycle with more frames was created to provide a more accurate consideration of carotid wall motion compared to the typical B-mode ultrasound ima-ges. The results revealed that our new hybrid method outperforms the pseudo-increasing frame rate scheme based on manifold learning.

Study of the effects of age and body mass index on the carotid wall vibration: extraction methodology and analysis.

This study aims to non-invasively extract the vibrations of the carotid wall and evaluate the changes in the carotid artery wall caused by age and obesity. Such evaluation can increase the possibility of detecting wall stiffness and atherosclerosis in its early stage. In this study, a novel method that uses a phase-tracking method based on the continuous wavelet transform calculates the carotid wall motion from the ultrasound radio frequency signals. To extract the high-frequency components of the wall motion, wall vibration, the empirical mode decomposition was then used. The posterior wall (intima-media) motion and vibration were extracted for 54 healthy volunteers (mean age: 33.87 ± 14.73 years), including 13 overweight subjects (body mass index > 25) and 14 female participants using their radio frequency signals. The results showed that the dominant frequency of the wall vibration correlates with age (r = -0.5887, p < 0.001) and body mass index (r = -0.4838, p < 0.001). The quantitative analysis further demonstrated that the dominant frequency of the vibration in the radial direction of the carotid wall decreases by age and is lower in overweight subjects. Besides, the peak-to-peak amplitude of the wall vibration showed significant correlations with age (r = -0.5456, p < 0.001) and body mass index (r = -0.5821, p < 0.001). The peak-to-peak amplitude also decreases by age and is lower in overweight subjects. However, there were no significant correlations between these features of the wall vibrations and systolic/diastolic blood pressure and sex. Our proposed measures were certified using the calculated arterial stiffness indices. The average power spectrum of the elderly subjects'wall motion in the frequency range of the wall vibration (>100 Hz) is decreased more in comparison with the young subjects. Our results revealed that the proposed method may be useful for detecting the stiffness and distortion in the carotid wall that occur prior to wall thickening caused by age as an early-stage atherosclerotic sign.

An optimization based approach embedded in a fuzzy connectivity algorithm for airway tree segmentation.

The main problem with airway segmentation methods which significantly influences their accuracy is leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. This phenomenon potentially makes large regions of lung-parenchyma to be wrongly identified as airways. A solution to this problem in the previous methods was based on leak detection followed by reducing leakage during the segmentation process. This has been dealt with adjusting the segmentation parameters and performing the re-segmentation process on the pre-segmented area. This makes the algorithm very exhaustive and more dependent on the user interaction. The method presented here is to apply a mathematical shape optimization approach embedded in an efficient fuzzy connectivity based segmentation algorithm to preserve shape features of the object to perform a precise segmentation. The novelty of our proposed scheme is to prevent the leakage root rather than taking leak detection and reduction approaches. This is carried out by minimizing a gradient based cost function which employs shape features of circle/cylindrical structure of airway tree during the process of segmentation.