Describes a new region-growing method for segmenting medical images. The method uses a closed snake driven by a pressure force that is a function of the statistical characteristics of image data. This statistical snake expands until it encounters pixels that lie outside user-defined limits relative to a seed region; when these limits are violated the pressure force is reversed to make the model contract. Tension and stiffness forces keep the boundary of the region model smooth, and a repulsion force prevents self-intersection. Boundary elements can be added and removed in response to complexity changes, and the tension, stiffness and pressure parameters can be adjusted to preserve the energy balance of the changing model. Statistical snakes have been used to reconstruct various anatomical features from NMR and CT volumes.<>
Active region models for segmenting medical images
Proceedings of 1st International Conference on Image Processing ; 2 ; 227-231 vol.2
1994-01-01
399609 byte
Conference paper
Electronic Resource
English
Active Region Models for Segmenting Medical Images
British Library Conference Proceedings | 1994
|Active region models for segmenting textures and colours
British Library Online Contents | 1995
|An algorithm for segmenting cytological images
British Library Online Contents | 2008
|A two-level method for segmenting cytological images based on active contour model
British Library Online Contents | 2008
|Segmenting Non Stationary Images with Triplet Markov Fields
British Library Conference Proceedings | 2005
|