Automatic Left Ventricle Segmentation by Edge Classification and Region Growing on Cardiac MRI 


Vol. 15,  No. 6, pp. 507-516, Dec.  2008
10.3745/KIPSTB.2008.15.6.507


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  Abstract

Cardiac disease is the leading cause of death in the world. Quantification of cardiac function is performed by manually calculating blood volume and ejection fraction in routine clinical practice, but it requires high computational costs. In this study, an automatic left ventricle (LV) segmentation algorithm using short-axis cine cardiac MRI is presented. We compensate coil sensitivity of magnitude images depending on coil location, classify edge information after extracting edges, and segment LV by applying region-growing segmentation. We design a weighting function for intensity signal and calculate a blood volume of LV considering partial voxel effects. Using cardiac cine SSFP of 38 subjects with Cornell University IRB approval, we compared our algorithm to manual contour tracing and MASS software. Without partial volume effects, we achieved segmentation accuracy of 3.3mL±5.8 (standard deviation) and 3.2mL±4.3 in diastolic and systolic phases, respectively. With partial volume effects, the accuracy was 19.1mL±8.8 and 10.3mL±6.1 in diastolic and systolic phases, respectively. Also in ejection fraction, the accuracy was -1.3%±2.6 and -2.1%±2.4 without and with partial volume effects, respectively. Results support that the proposed algorithm is exact and useful for clinical practice.

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[IEEE Style]

H. Y. Lee, "Automatic Left Ventricle Segmentation by Edge Classification and Region Growing on Cardiac MRI," The KIPS Transactions:PartB , vol. 15, no. 6, pp. 507-516, 2008. DOI: 10.3745/KIPSTB.2008.15.6.507.

[ACM Style]

Hae Yeoun Lee. 2008. Automatic Left Ventricle Segmentation by Edge Classification and Region Growing on Cardiac MRI. The KIPS Transactions:PartB , 15, 6, (2008), 507-516. DOI: 10.3745/KIPSTB.2008.15.6.507.

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