PROJECT TITLE :

Automatic Fascicle Length Estimation on Muscle Ultrasound Images With an Orientation-Sensitive Segmentation

ABSTRACT:

Goal: The fascicle length obtained by ultrasound imaging is one among the crucial muscle design parameters for understanding the contraction mechanics and pathological conditions of muscles. But, the shortage of a reliable automatic measurement method restricts the application of the fascicle length for the analysis of the muscle function, as frame-by-frame manual measurement is time-consuming. In this study, we tend to propose an automatic measurement method to preclude the influence of nonfascicle components on the estimation of the fascicle length by using motion estimation of fascicle structures. Ways: The strategy starts with image segmentation using the cohesiveness of fascicle orientation as a feature, getting the fascicle amendment by tracking manually marked points on the fascicular path with the Lucas–Kanade optical flow algorithm applied on the segmented image. Results: The performance of this methodology was evaluated on ultrasound images of the gastrocnemius obtained from seven healthy subjects (34.four ± 5.zero years). Waveform similarity between the manual and dynamic measurements was assessed by calculating the similarity with the coefficient of multiple correlations (CMC). In vivo experiments demonstrated that fascicle tracking with the orientation-sensitive segmentation (CMC = zero.ninety seven ± zero.01) was a lot of in step with the manual measurements than existing automatic strategies (CMC = zero.87 ± zero.10). Conclusion: Our method was strong to the interference of nonfascicle elements, resulting in an exceedingly additional reliable measurement of the fascicle length. Significance: The proposed methodology could facilitate additional analysis and applications related to real-time architectural modification of muscles.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE :Smart Trailer : Automatic generation of movie trailer using only subtitles - 2018ABSTRACT:With the large growth rate in user-generated videos, it is changing into increasingly important to be able to navigate them
PROJECT TITLE :New Automatic Modulation Classifier Using Cyclic-Spectrum Graphs With Optimal Training Features - 2018ABSTRACT:A new feature-extraction paradigm for graph-based automatic modulation classification is proposed in
PROJECT TITLE :Automatic Modulation Classification Using Moments and Likelihood Maximization - 2018ABSTRACT:Motivated by the fact that moments of the received signal are easy to compute and can give a simple means to automatically
PROJECT TITLE :Automatic Feature Selection Technique for Next Generation Self-Organizing Networks - 2018ABSTRACT:Despite self-organizing networks (SONs) pursue the automation of management tasks in current cellular networks, the
PROJECT TITLE :Automatic Registration of Images With Inconsistent Content Through Line-Support Region Segmentation and Geometrical Outlier Remova - 2018ABSTRACT:The implementation of automatic image registration is still troublesome

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry