Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set.
An automatic framework is proposed to segment right ventricle on ultrasound images. This method can automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to automatically detect the main location of the right ventricle and the corresponding transform relationship between the training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle from the whole series. Experimental results from real subject data validated the performance of the proposed framework in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial boundaries are 89.1%±2.3% and 83.6±7.3%, respectively. The automatic segmentation method based on sparse matrix transform and level set can provide a useful tool for quantitative cardiac imaging.
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA.
Xulei
X
Qin
Zhibin
Z
Cong
Luma V
LV
Halig
Baowei
B
Fei
Image segmentation; cardiac imaging; functional imaging; genetic algorithm; heart; level set; myocardium; right ventricle; sparse matrix transform; ultrasound imaging