Spatio-temporal image segmentation using optical flow and clustering algorithm
Sasa Galic, Sven Lončarić · 2002
Image segmentation is an important and challenging problem in image analysis. Segmentation of moving objects in image sequences is even more difficult and computationally expensive. In this work we propose a technique for spatio-temporal segmentation of medical image sequences based on clustering in the feature vector space. The motivation for the spatio-temporal approach is the fact that motion is a useful clue for object segmentation. A two-dimensional feature vector has been used for clustering in the feature space. The first feature is image brightness which reveals the structure of interest in the image. The second feature is the Euclidean norm of the optical flow vector. The optical flow field is computed using a Horn-Schunck algorithm. By clustering in the feature space, it is possible to detect a moving object in the image. Experiments have been conducted using a sequence of ECG-gated magnetic resonance (MR) images of a beating heart. The method is also tested on images with moving background. The experiments have shown encouraging results.