Segmentation of abdomen MR images using kernel graph cuts with shape priors

• Published in: Biomedical engineering online Vol. 12; no. 1; p. 124
• Main Authors: Luo, Qing, Qin, Wenjian, Wen, Tiexiang, Gu, Jia, Gaio, Nikolas, Chen, Shifu, Li, Ling, Xie, Yaoqin
• Format: Journal Article
• Language: English
• Published: London BioMed Central 03.12.2013; BioMed Central Ltd; Springer Nature B.V
• Subjects: Abdomen; Algorithms; Bioengineering; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Colleges & universities; Engineering; Engineering research; Experiments; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Liver; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging equipment; Neural networks; NMR; Nuclear magnetic resonance; Principal Component Analysis; Reproducibility of Results; Surgery; Weak Boundary; Shape Prior; Abdominal Organ; Shape Template; Initial Contour
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/1475-925X-12-124

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.