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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

Examples of volumetric histograms in range [15, 600] HU. (a) A high contrast image with two high peaks. (b) A low contrast image with only one high peak.
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fig3: Examples of volumetric histograms in range [15, 600] HU. (a) A high contrast image with two high peaks. (b) A low contrast image with only one high peak.

Mentions: By analyzing the volumetric histogram of IROA, IROA could be classified as high contrast or low contrast [34]. The image with two high peaks in the histogram was regarded as high contrast (Figure 3(a)). If only one high peak was detected in the histogram, the image was regarded as low contrast (Figure 3(b)).


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)

Examples of volumetric histograms in range [15, 600] HU. (a) A high contrast image with two high peaks. (b) A low contrast image with only one high peak.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Examples of volumetric histograms in range [15, 600] HU. (a) A high contrast image with two high peaks. (b) A low contrast image with only one high peak.
Mentions: By analyzing the volumetric histogram of IROA, IROA could be classified as high contrast or low contrast [34]. The image with two high peaks in the histogram was regarded as high contrast (Figure 3(a)). If only one high peak was detected in the histogram, the image was regarded as low contrast (Figure 3(b)).

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