Abstract
In the domain of image processing, Image Segmentation plays an essential role. Digital images are divided into segments, which are composed of sets of pixels, using a segmentation algorithm in order to study significant data and information from the digital images. Segmentation makes information retrieval easier from the region of interest. It assists in conversion of the digital images into more meaningful set of information that is relatively easy to examine. In recent years, segmentation algorithms have steadily gained popularity in medical field as well as it has witnessed successful applications of Neutrosophy. This paper outlines a comparative study and a comprehensive literature review of existing and novel segmentation methods proposed recently. The literature reviews have revealed a strong preference for hybrid models and deep learning techniques as diagnostic and treatment tools.
Published Version
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.
