Development Of Semi-Automatic Liver Segmentation Method For Three-Dimensional Computed Tomography Dataset
Segmentation of liver from 3D computed tomography (CT) dataset is very important in hepatic disease diagnosis and treatment planning. Manual segmentation gives accurate result but the process is tedious and time-consuming due to a large number of slices produced by the CT scanner. Low contrast of...
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| Main Author: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2017
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| Subjects: | |
| Online Access: | http://eprints.usm.my/53024/1/Development%20Of%20Semi-Automatic%20Liver%20Segmentation%20Method%20For%20Three-Dimensional%20Computed%20Tomography%20Dataset_Chiang%20Yi%20Fan_E3_2017.pdf http://eprints.usm.my/53024/ |
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| Summary: | Segmentation of liver from 3D computed tomography (CT) dataset is very important in
hepatic disease diagnosis and treatment planning. Manual segmentation gives accurate result but
the process is tedious and time-consuming due to a large number of slices produced by the CT
scanner. Low contrast of liver boundary with neighbouring organs, high shape variability of liver
and presence of various liver pathologies will affect the accuracy of automatic liver segmentation
and thus make automatic liver segmentation a challenging task. Therefore, a semi-automated liver
segmentation program is developed in this project in order to obtain high accuracy in liver
segmentation and reduce the time required for manual liver segmentation. The proposed
algorithm can be divided into three stages. The first stage is parameter setup and pre-processing.
User interaction is required to setup the segmentation parameters. For pre-processing, anisotropic
diffusion filtering is applied to reduce noise in the image and smooth the image. In second stage,
thresholding is applied to CT images to extract the possible liver regions. Then, morphological
closing and opening are used close small holes inside liver region and break the thin connections
between liver and neighbouring organs. Hole-filling is employed to fill up the large holes inside
liver region. Next, the connected component analysis is performed to extract liver region from
the CT slices. The last stage is post-processing. In post-processing, the contour of liver is smooth
by binary Gaussian filter. The liver segmentation program with proposed algorithm is evaluated
with CT datasets obtained from SLIVER07 to prove its effectiveness in liver segmentation. The
results of liver segmentation achieved average VOE of 9.93 |
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