Segmentation of objects in temporal images using the hidden Markov model

Automatic segmentation of objects in images is an ongoing research problem with applications in many fields. If a scene is imaged serially over time, an advantage can be gained by using segmentation results from previous and subsequent images when segmenting the current image. This paper discusses a probabilistic framework for making use of temporal information in the segmentation process. A subset of dynamic Bayesian networks, the hidden Markov model is described as a means to improve segmentation over statistical classification techniques that use static pixel intensity information alone. An application of this technique to the segmentation of tumors in magnetic resonance images (MRIs) is described. The segmentation accuracy was increased compared to a popular 3D spatial only segmentation method.