Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach.
The liver vessels, which have low signal and run next to brighter bile ducts, are difficult to segment from MR images. This study presents a fully automated and adaptive method to segment portal and hepatic veins on magnetic resonance images. In the proposed approach, segmentation of these vessels is achieved in four stages: (i) initial segmentation, (ii) refinement, (iii) reconstruction, and (iv) post-processing. In the initial segmentation stage, k-means clustering is used, the results of which are refined iteratively with linear contrast stretching algorithm in the next stage, generating a mask image. In the reconstruction stage, vessel regions are reconstructed with the marker image from the first stage and the mask image from the second stage. Experimental data sets include slices that show fat tissues, which have the same gray level values with vessels, outside the margin of the liver. These structures are removed in the last stage. Results show that the proposed approach is more efficient than other thresholding-based methods. Copyright © 2016 John Wiley & Sons, Ltd.
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
Evgin
E
Goceri
Department of Radiology, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.
Zarine K
ZK
Shah
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
Metin N
MN
Gurcan
D000005:Abdomen / Q000098:blood supply / Q000000981:diagnostic imaging*; D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000098:blood supply / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*
MR images; hepatic veins; portal veins; vessel segmentation