Abstract
Objectives: A new method for automatic segmentation is designed for improving accuracy and abnormality detection in fetus Ultra Sound images at an early stage. Methods: Curvelet-based Seed Point Selection (S-CSPS) method is developed for improving abnormality detection rate using 𝑘-means segmentation algorithm. Correct identification of regions for each pixel that belongs to the objects in US images is obtained through seed point evaluation reducing the speckle and therefore resulting in the improvement of abnormality being detected. Findings: The proposed S-CSPS is implemented in MATLAB platform using ultrasound images of anomalies of fetal spine dataset. The performance analysis is conducted on factors such as noise, segmentation accuracy abnormality detection rate and segmentation time with respect to a different number of fetus images. Improvement: The simulation analysis results shows that S-CSPS method offers better performance with an improvement of segmentation accuracy and improving the abnormality detection rate compared to state-of-the art methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
