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Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.

Wu W, Zhou Z, Wu S, Zhang Y - Comput Math Methods Med (2016)

Bottom Line: Despite many years of research, automatic liver segmentation remains a challenging task.Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm.Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

View Article: PubMed Central - PubMed

Affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China.

ABSTRACT
Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

No MeSH data available.


Related in: MedlinePlus

An example of liver segmentation using the proposed method. (a) and (b) show the 3D abdominal region extraction. (c) The selected largest liver slice with initial liver region in blue contour and background regions in red contours. From (c) to (f), orange rectangles show the liver VOI. (d) and (e) show the heart and kidney slices, respectively, with additional background regions in red contours. (f) The segmented result after postprocessing in yellow contour. (g) The reconstructed 3D liver volume.
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fig6: An example of liver segmentation using the proposed method. (a) and (b) show the 3D abdominal region extraction. (c) The selected largest liver slice with initial liver region in blue contour and background regions in red contours. From (c) to (f), orange rectangles show the liver VOI. (d) and (e) show the heart and kidney slices, respectively, with additional background regions in red contours. (f) The segmented result after postprocessing in yellow contour. (g) The reconstructed 3D liver volume.

Mentions: Figure 6 illustrates an example of liver segmentation using the proposed method. Figures 6(a) and 6(b) show the 3D abdominal region extraction using MIP and thresholding methods. In Figures 6(c)–6(f), the orange rectangles show the extracted liver VOI, which was obtained by analyzing the histogram and by using adaptive thresholding and morphological methods. Figure 6(c) shows the largest liver slice with initial liver region in blue and background region in red. Foreground and background seed points were sampled in the liver and background region, respectively. To avoid oversegmentations of heart and kidney, additional background seeds were extracted on the heart and kidney slices, as shown in Figures 6(e) and 6(f). The segmented liver after postprocessing is shown as the yellow contour in Figure 6(f). Figure 6(g) shows the 3D liver volume reconstructed by using surface rendering algorithms in VTK.


Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts.

Wu W, Zhou Z, Wu S, Zhang Y - Comput Math Methods Med (2016)

An example of liver segmentation using the proposed method. (a) and (b) show the 3D abdominal region extraction. (c) The selected largest liver slice with initial liver region in blue contour and background regions in red contours. From (c) to (f), orange rectangles show the liver VOI. (d) and (e) show the heart and kidney slices, respectively, with additional background regions in red contours. (f) The segmented result after postprocessing in yellow contour. (g) The reconstructed 3D liver volume.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4835633&req=5

fig6: An example of liver segmentation using the proposed method. (a) and (b) show the 3D abdominal region extraction. (c) The selected largest liver slice with initial liver region in blue contour and background regions in red contours. From (c) to (f), orange rectangles show the liver VOI. (d) and (e) show the heart and kidney slices, respectively, with additional background regions in red contours. (f) The segmented result after postprocessing in yellow contour. (g) The reconstructed 3D liver volume.
Mentions: Figure 6 illustrates an example of liver segmentation using the proposed method. Figures 6(a) and 6(b) show the 3D abdominal region extraction using MIP and thresholding methods. In Figures 6(c)–6(f), the orange rectangles show the extracted liver VOI, which was obtained by analyzing the histogram and by using adaptive thresholding and morphological methods. Figure 6(c) shows the largest liver slice with initial liver region in blue and background region in red. Foreground and background seed points were sampled in the liver and background region, respectively. To avoid oversegmentations of heart and kidney, additional background seeds were extracted on the heart and kidney slices, as shown in Figures 6(e) and 6(f). The segmented liver after postprocessing is shown as the yellow contour in Figure 6(f). Figure 6(g) shows the 3D liver volume reconstructed by using surface rendering algorithms in VTK.

Bottom Line: Despite many years of research, automatic liver segmentation remains a challenging task.Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm.Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

View Article: PubMed Central - PubMed

Affiliation: College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China.

ABSTRACT
Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

No MeSH data available.


Related in: MedlinePlus