• The KIPS Transactions:PartA
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• v.14A no.2
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• pp.77-84
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• 2007

The intima media thickness(IMT) of the carotid artery from B mode ultrasound images has recently been proposed as the most useful index of individual atherosclerosis and can be used to predict major cardiovascular events. Ultrasonic measurements of the IMT are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the inter and intra observer variability and its inefficiency. In this paper, we present a multiresolution snake method combined with the dynamic programming, which overcomes the various noises and sensitivity to initialization of conventional snake. First, an image pyramid is constructed using the Gaussian pyramid that maintains global edge information with smoothing in the images, and then the boundaries are automatically detected in the lowest resolution level by minimizing a cost function based on dynamic programming. The cost function includes cost terms which are representing image features and geometrical continuity of the vessel interfaces. Since the detected boundaries are selected as initial contour of the snake for the next level, this automated approach solves the problem of the initialization. Moreover, the proposed snake improves the problem of converging th the local minima by defining the external energy based on multiple image features. In this paper, our method has been validated by computing the correlation between manual and automatic measurements. This automated detection method has obtained more accurate and reproducible results than conventional edge detection by considering multiple image features.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.507-516
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• 2008

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 $3.3mL{\pm}5.8$ (standard deviation) and $3.2mL{\pm}4.3$ in diastolic and systolic phases, respectively. With partial volume effects, the accuracy was $19.1mL{\pm}8.8$ and $10.3mL{\pm}6.1$ in diastolic and systolic phases, respectively. Also in ejection fraction, the accuracy was $-1.3%{\pm}2.6$ and $-2.1%{\pm}2.4$ without and with partial volume effects, respectively. Results support that the proposed algorithm is exact and useful for clinical practice.