Neda Sangsefidi1, Amir Hossein Foruzan2, Ardeshir Dolati3. 1. Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran. Electronic address: n.sangsefidi@shahed.ac.ir. 2. Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran. Electronic address: a.foruzan@shahed.ac.ir. 3. Department of Computer Science, Shahed University, Tehran, Iran. Electronic address: dolati@shahed.ac.ir.
Abstract
PURPOSE: The accurate delineation of hepatic vessels is important to diagnosis and treatment planning. To improve the segmentation of these vessels and extract small structures, we adaptively calculate the data term in conventional graph-cuts algorithm. METHOD: To assign higher costs to the data term in small vessel regions, we estimate the statistical parameters of the vessel adaptively. After preprocessing an input CT image, we model the liver and its vessels by two Gaussian distributions. The Maximum Intensity Projection (MIP) of the image is employed in the Expectation-Maximization algorithm to estimate the parameters of the model. These parameters are used together with a medial-axes enhancement algorithm to find the axes of the vessels. The skeleton of these vessels is considered to be the image voxels that are most similar to the hepatic vascular structures. To calculate the cost function of the graph-cuts algorithm, those axes that are nearby are employed to estimate the vessel parameters. The conventional minimum-cut/maximum-flow energy minimization framework finds the global minimum of the cost function and labels vessel voxels. RESULT: We evaluated our method using synthetic data and clinical images. We compared our algorithm with state-of-the-art vessel segmentation methods. The mean Dice measure of our results was 95.51% (0.9% lower than the first rank method). Quantitatively, our method segmented small hepatic vessels that were not extracted by traditional techniques including conventional graph-cuts. CONCLUSION: The proposed method improved the segmentation of small vessels in the presence of noise.
PURPOSE: The accurate delineation of hepatic vessels is important to diagnosis and treatment planning. To improve the segmentation of these vessels and extract small structures, we adaptively calculate the data term in conventional graph-cuts algorithm. METHOD: To assign higher costs to the data term in small vessel regions, we estimate the statistical parameters of the vessel adaptively. After preprocessing an input CT image, we model the liver and its vessels by two Gaussian distributions. The Maximum Intensity Projection (MIP) of the image is employed in the Expectation-Maximization algorithm to estimate the parameters of the model. These parameters are used together with a medial-axes enhancement algorithm to find the axes of the vessels. The skeleton of these vessels is considered to be the image voxels that are most similar to the hepatic vascular structures. To calculate the cost function of the graph-cuts algorithm, those axes that are nearby are employed to estimate the vessel parameters. The conventional minimum-cut/maximum-flow energy minimization framework finds the global minimum of the cost function and labels vessel voxels. RESULT: We evaluated our method using synthetic data and clinical images. We compared our algorithm with state-of-the-art vessel segmentation methods. The mean Dice measure of our results was 95.51% (0.9% lower than the first rank method). Quantitatively, our method segmented small hepatic vessels that were not extracted by traditional techniques including conventional graph-cuts. CONCLUSION: The proposed method improved the segmentation of small vessels in the presence of noise.