Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations

A sequential segmentation framework, where objects in an image are successively segmented, generally raises some questions about the “best” segmentation sequence to follow and/or how to avoid error propagation. In this work, we propose original approaches to answer these questions in the case where the objects to segment are represented by a model describing the spatial relations between objects. The process is guided by a criterion derived from visual attention, and more precisely from a saliency map, along with some spatial information to focus the attention. This criterion is used to optimize the segmentation sequence. Spatial knowledge is also used to ensure the consistency of the results and to allow backtracking on the segmentation order if needed. The proposed approach was applied for the segmentation of internal brain structures in magnetic resonance images. The results show the relevance of the optimization criteria and the interest of the backtracking procedure to guarantee good and consistent results.

► Sequential segmentation and recognition of objects in images based on a model. ► Process guided by a new combination of visual attention models and spatial reasoning. ► Saliency and spatial relations to define the best segmentation sequence in each image. ► Original backtracking strategy and consistency criteria to avoid error propagation. ► Progressive and automatic specialization of the model to each image.