Left ventricle surface reconstruction from volumetric CT images by the fusion of clustering and active contours

This paper presents an integrated scheme to extract and reconstruct left ventricle chambers from CT volumetric image sequences. An accurate extraction of left ventricle chambers is a crucial step towards cardiac dynamics analysis based on image sequences, a very much desired non-invasive technique for heart disease diagnosis and monitoring. The integrated approach aims at solving two major problems in cardiac image segmentation: imaging related ambiguity and anatomy related ambiguity. The K-means clustering with Gibb's random field constraints is able to resolve the imaging related ambiguity to obtain robust segmentation even when the intensity of the left ventricle exhibits spatially varying distribution. The active contour models incorporating a priori shape knowledge is able to resolve the anatomy related ambiguity to estimate the valve that separates the left ventricle from left atrium and aorta but is indistinguishable in the given images due to motion and partial volume effects. The fusion of the clustering and active contour models enables an integrated reconstruction of left ventricle chambers from the CT image sequences. Preliminary results show that the proposed scheme can produce extracted left ventricle chambers that compare favorably with the manually delineated chambers by a skilled operator. However, this proposed scheme is fast and reproducible.