Segmentation of magnetic resonance images using discrete curve evolution and fuzzy clustering

Simple item page
dc.contributor.authorSupot, Sookpotharom
dc.contributor.authorThanapong, Chaichana
dc.contributor.authorChuchart, Pintavirooj
dc.contributor.authorManas, Sangworasil
dc.date.accessioned2026-08-06T09:56:08Z
dc.date.available2026-08-06T09:56:08Z
dc.date.issued2007-12-01
dc.description.abstractThe region clustering of a Magnetic Resonance Imaging (MRI) image is more complicate than a Computed Topography (CT) image because a MRI image composes of three components such as T1-weighted, T2-weighted, and Proton Density (PD) in each layer. However, the MRI images provide more detail than the CT images. Therefore, we propose a technique of the region clustering of MRI image by using Fuzzy c-means (FCM). The fuzzy c-means algorithm is an iterative operation, that is very time-consuming and makes the algorithm impractical for using in image segmentation. To cope with this problem, the discrete curve evolution (DCE) technique is applied to find the actual cluster center to refine the initial value of the fuzzy c-means algorithm, which reduces the convergence time. In experimental results, the proposed technique provides the same segmentation accuracy as the fuzzy c-means technique. Moreover, this technique takes lower computational time comparing to the previous method. © 2007 IEEE.
dc.identifier.citationIEEE Icit 2007 2007 IEEE International Conference on Integration Technology, 697-700, 2007
dc.identifier.doi10.1109/ICITECHNOLOGY.2007.4290409
dc.identifier.other2-s2.0-46449107351
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/1876
dc.sourceIEEE Icit 2007 2007 IEEE International Conference on Integration Technology
dc.subjectClustering
dc.subjectCT
dc.subjectDiscrete curve evolution
dc.subjectFuzzy c-means
dc.subjectMRI
dc.subjectSegmentation
dc.titleSegmentation of magnetic resonance images using discrete curve evolution and fuzzy clustering
dc.typeConference Paper

Files

Collections

Research