Delineating anatomical boundaries using the boundary fragment model.
In this paper we present a method to automatically isolate relevant anatomical boundary positions in an image using only the structure of edges. The purpose of this method is to facilitate model-based segmentation algorithms which rely on accurate initialisation and assume that the correct anatomical boundary positions are close to the current model surface. The method is built around a weak parts-based shape model - the Boundary Fragment Model (BFM) - which represents an object by sections of its boundary. Following previous literature, we use the BFM in a boosted classifier framework to first automatically detect the object of interest. Extending previous work, we use the BFM to drive a classifier which isolates boundary candidates from spurious and irrelevant edge responses. The application of our algorithm leads to a labelled edge map which encodes the positions of (multiple) object boundaries. By way of illustrating what is a general solution, the task of identifying the endocardium and epicardium in three-dimensional ultrasound images is completely examined, including a detailed analysis of the parameters which impact on the model construction, the structure of the learned edge response classifier, and implementation concerns. For completeness, we also demonstrate how the output boundary positions can be used in a full model-based segmentation framework.
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom. Electronic address: richard.stebbing@eng.ox.ac.uk.
Richard V
RV
Stebbing
J Alison
JA
Noble
D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006321:Heart; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008953:Models, Anatomic; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
Machine learning; Segmentation; Shape model