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Segmentation of abdomen MR images using kernel graph cuts with shape priors
Qing Luo, Wenjian Qin, Tiexiang Wen
1The Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P, R, China. jia.gu@siat.ac.cn.
Background:
Abdominal organs segmentation of magnetic resonance (MR) images is an important but challenging task in medical image processing. Especially for abdominal tissues or organs, such as liver and kidney, MR imaging is a very difficult task due to the fact that MR images are affected by intensity inhomogeneity, weak boundary, noise and the presence of similar objects close to each other.
Method:
In this study, a novel method for tissue or organ segmentation in abdomen MR imaging is proposed; this method combines kernel graph cuts (KGC) with shape priors. First, the region growing algorithm and morphology operations are used to obtain the initial contour. Second, shape priors are obtained by training the shape templates, which were collected from different human subjects with kernel principle component analysis (KPCA) after the registration between all the shape templates and the initial contour. Finally, a new model is constructed by integrating the shape priors into the kernel graph cuts energy function. The entire process aims to obtain an accurate image segmentation.
Results:
The proposed segmentation method has been applied to abdominal organs MR images. The results showed that a satisfying segmentation without boundary leakage and segmentation incorrect can be obtained also in presence of similar tissues. Quantitative experiments were conducted for comparing the proposed segmentation with other three methods: DRLSE, initial erosion contour and KGC without shape priors. The comparison is based on two quantitative performance measurements: the probabilistic rand index (PRI) and the variation of information (VoI). The proposed method has the highest PRI value (0.9912, 0.9983 and 0.9980 for liver, right kidney and left kidney respectively) and the lowest VoI values (1.6193, 0.3205 and 0.3217 for liver, right kidney and left kidney respectively).
Conclusion:
The proposed method can overcome boundary leakage. Moreover it can segment liver and kidneys in abdominal MR images without segmentation errors due to the presence of similar tissues. The shape priors based on KPCA was integrated into fully automatic graph cuts algorithm (KGC) to make the segmentation algorithm become more robust and accurate. Furthermore, if a shelter is placed onto the target boundary, the proposed method can still obtain satisfying segmentation results.
Insights
This study introduces a novel method combining kernel graph cuts (KGC) with shape priors for accurate abdominal organ segmentation in MR images, overcoming common challenges like boundary leakage and similar tissues.
Area of Science:
- Medical Image Analysis
- Computer Vision
- Biomedical Engineering
Background:
- Abdominal organ segmentation in Magnetic Resonance (MR) imaging presents significant challenges.
- Issues include intensity inhomogeneity, weak boundaries, noise, and the presence of similar adjacent tissues.
Purpose of the Study:
- To propose a novel and accurate method for abdominal organ segmentation in MR images.
- To address limitations of existing segmentation techniques, particularly boundary leakage and errors caused by similar tissues.
Main Methods:
- A hybrid approach combining Kernel Graph Cuts (KGC) with shape priors.
- Initial contour generation using region growing and morphology operations.
- Shape priors derived using Kernel Principal Component Analysis (KPCA) on registered shape templates.
- Integration of shape priors into the KGC energy function for robust segmentation.
Main Results:
- The proposed method achieved satisfying segmentation of abdominal organs (liver, kidneys) without boundary leakage or errors from similar tissues.
- Quantitative comparison using Probabilistic Rand Index (PRI) and Variation of Information (VoI) demonstrated superior performance.
- Achieved highest PRI (up to 0.9983) and lowest VoI (down to 0.3205) compared to other methods.
Conclusions:
- The novel KGC with shape priors method effectively overcomes boundary leakage and segmentation errors in abdominal MR images.
- Integration of KPCA-based shape priors enhances the robustness and accuracy of the graph cuts algorithm.
- The method provides reliable segmentation even when dealing with challenging image artifacts and tissue similarities.
