Abstract
Magnetic Resonance Imaging (MRI) has become a widely used modality because it produces multispectral image sequences that provide information of free water, proteinaceous fluid, soft tissue and other tissues with a variety of contrast. The abundance fractions of tissue signatures provided by multispectral images can be very useful for medical diagnosis compared to other modalities. Multiple Sclerosis (MS) is thought to be a disease in which the patient immune system damages the isolating layer of myelin around the nerve fibers. This nerve damage is visible in Magnetic Resonance (MR) scans of the brain. Manual segmentation is extremely time consuming and tedious. Therefore, fully automated MS detection methods are being developed which can classify large amounts of MR data, and do not suffer from inter observer variability. In this paper, we propose two intelligent segmentation methods, fuzzy c-mean and Geodesic Active Contours of Caselles level set method to do the MR image segmentation jobs so as to find the effect they yield. The results show those intelligent methods both do a pretty job than other common image segmentation algorithm.KeywordsMagnetic Resonance Imaging (MRI)medical image segmentationmultispectral image sequencesfuzzy c-mean (FCM)Caselles level set method
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.