Article ID Journal Published Year Pages File Type
525785 Computer Vision and Image Understanding 2013 9 Pages PDF
Abstract

•Challenging segmentation of the right ventricle in cardiac MR images.•Use of a statistical shape model based on a signed distance function in order to constrain the segmentation.•The shape prior is introduced into a graph cut approach.•Results are comparable to the state-of-the-art in RV segmentation.

Segmenting the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. The segmentation of the RV is a difficult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a segmentation method based on a statistical shape model obtained with a principal component analysis (PCA) on a set of representative shapes of the RV. Shapes are not represented by a set of points, but by distance maps to their contour, relaxing the need for a costly landmark detection and matching process. A shape model is thus obtained by computing a PCA on the shape variations. This prior is registered onto the image via a very simple user interaction and then incorporated into the well-known graph cut framework in order to guide the segmentation. Our semi-automatic segmentation method has been applied on 248 MR images of a publicly available dataset (from MICCAI’12 Right Ventricle Segmentation Challenge). We show that encouraging results can be obtained for this challenging application.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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