Automatic right ventricle segmentation in CT images using a novel multi-scale edge detector approach

We present a novel approach for the automatic segmentation of the right ventricle in CT images. We use a level set with a new multi-scale edge stopping function based on spatial oriented filters. This stopping function reduces false edge detection and over-segmentation. The segmentation method was evaluated over 18 CT image studies from healthy and pathologic subjects; results are compared against manual segmentation made by a team of expert radiologists. The mean surface distance error is below 0.64 mm, which proves the effectiveness of the method.

[1]  Herbert Edelsbrunner,et al.  Three-dimensional alpha shapes , 1992, VVS.

[2]  Anthony J. Yezzi,et al.  Active geodesics: Region-based active contour segmentation with a global edge-based constraint , 2011, 2011 International Conference on Computer Vision.

[3]  Caroline Petitjean,et al.  A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..

[4]  Piotr J. Slomka,et al.  Heart chambers and whole heart segmentation techniques: review , 2012, J. Electronic Imaging.

[5]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Caroline Petitjean,et al.  Automatic cardiac ventricle segmentation in MR images: a validation study , 2011, International Journal of Computer Assisted Radiology and Surgery.

[7]  Dorin Comaniciu,et al.  Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features , 2008, IEEE Transactions on Medical Imaging.

[8]  H ARITHA,et al.  A Boundary Detection in Medical Images using Edge Following Algorithm Based on Intensity Gradient and Texture Gradient Features , 2015 .

[9]  Nikos Paragios,et al.  A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis , 2002, International Journal of Computer Vision.

[10]  Paul F. Whelan,et al.  Automatic segmentation of the left ventricle cavity and myocardium in MRI data , 2006, Comput. Biol. Medicine.

[11]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  T van Walsum,et al.  Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and multivendor study. , 2010, Medical physics.

[13]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[14]  Olivier Ecabert,et al.  Segmentation of the heart and great vessels in CT images using a model-based adaptation framework , 2011, Medical Image Anal..

[15]  Andrew Zisserman,et al.  A Statistical Approach to Texture Classification from Single Images , 2004, International Journal of Computer Vision.