01882nas a2200157   4500008004100000245006400041210006300105260004200168300001400210520129900224100002801523700002601551700002201577700002301599856010201622       2000                        eng d00aFuzzy-connected 3D image segmentation at interactive speeds0 aFuzzyconnected 3D image segmentation at interactive speeds  aBellingham; WashingtonbSPIEc2000///  a212 - 2233 aImage segmentation techniques using fuzzy connectednessprinciples have shown their effectiveness in segmenting a 
variety of objects in several large applications in recent 
years. However, one problem with these algorithms has been their 
excessive computational requirements. In an attempt to 
substantially speed them up, in the present paper, we study 
systematically a host of 18 algorithms under two categories -- 
label correcting and label setting. Extensive testing of these 
algorithms on a variety of 3D medical images taken from large 
ongoing applications demonstrates that a 20 - 360 fold 
improvement over current speeds is achievable with a combination 
of algorithms and fast modern PCs. The reliable recognition 
(assisted by human operators) and the accurate, efficient, and 
sophisticated delineation (automatically performed by the 
computer) can be effectively incorporated into a single 
interactive process. If images having intensities with tissue 
specific meaning (such as CT or standardized MR images) are 
utilized, all parameters for the segmentation method can be 
fixed once for all, all intermediate data can be computed before 
the user interaction is needed, and the user can be provided 
with more information at the time of interaction.
1 aNyúl, László, Gábor1 aFalcao, Alexandre, X.1 aUdupa, Jayaram, K1 aHanson, Kenneth, M  uhttps://inf.u-szeged.hu/publication/fuzzy-connected-3d-image-segmentation-at-interactive-speeds-0