کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
525785 | 869025 | 2013 | 9 صفحه PDF | دانلود رایگان |

• 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.
Journal: Computer Vision and Image Understanding - Volume 117, Issue 9, September 2013, Pages 1027–1035