The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness"', or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.
Database-guided segmentation of anatomical structures with complex appearance
2005-01-01
728508 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Database-Guided Simultaneous Multi-slice 3D Segmentation for Volumetric Data
British Library Conference Proceedings | 2006
|Database-Guided Simultaneous Multi-slice 3D Segmentation for Volumetric Data
Springer Verlag | 2006
|A Classification approach for Anatomical Regions Segmentation
British Library Conference Proceedings | 2005
|Segmentation using Appearance of Mesostructure Roughness
British Library Online Contents | 2009
|