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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 VOI extraction and the largest liver slice selection. (a) Axial slices of the initial binary liver mask by using adaptive thresholding. (b) Axial slices of the processed binary liver mask. (c) The selected largest liver slice with the initial liver region in yellow and the liver VOI in red.
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fig4: An example of liver VOI extraction and the largest liver slice selection. (a) Axial slices of the initial binary liver mask by using adaptive thresholding. (b) Axial slices of the processed binary liver mask. (c) The selected largest liver slice with the initial liver region in yellow and the liver VOI in red.

Mentions: By applying binary thresholding [Tlower, Tupper] to IROA, the initial binary liver mask Iliver0 was obtained (Figure 4(a)).


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 VOI extraction and the largest liver slice selection. (a) Axial slices of the initial binary liver mask by using adaptive thresholding. (b) Axial slices of the processed binary liver mask. (c) The selected largest liver slice with the initial liver region in yellow and the liver VOI in red.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: An example of liver VOI extraction and the largest liver slice selection. (a) Axial slices of the initial binary liver mask by using adaptive thresholding. (b) Axial slices of the processed binary liver mask. (c) The selected largest liver slice with the initial liver region in yellow and the liver VOI in red.
Mentions: By applying binary thresholding [Tlower, Tupper] to IROA, the initial binary liver mask Iliver0 was obtained (Figure 4(a)).

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