Brain tissue MR-image segmentation via optimum-path forest clustering

• Published in: Computer vision and image understanding Vol. 116; no. 10; pp. 1047 - 1059
• Main Authors: Cappabianco, Fábio A.M., Falcão, Alexandre X., Yasuda, Clarissa L., Udupa, Jayaram K.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.10.2012; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Brain; Brain tissue segmentation; Clustering; Computer science; control theory; systems; Exact sciences and technology; Field inhomogeneity/bias correction; Forests; Graph-based methods; Image clustering; Investigative techniques, diagnostic techniques (general aspects); Labels; Magnetic resonance images; Medical image analysis; Medical sciences; Nervous system; Pattern recognition. Digital image processing. Computational geometry; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Robustness; Segmentation; Segmentation evaluation; Strategy; Segmentation evaluation; Brain tissue segmentation; Field inhomogeneity/bias correction; Magnetic resonance images; Medical image analysis; Image clustering; Graph-based methods; Cluster analysis; Brain; Image processing; Bias; Central nervous system; Optimization; Encephalon; Tissue; Completeness; Voxel; Classification; Robustness; Cluster; Graph theory; Anatomy; Nuclear magnetic resonance imaging; Image segmentation; Image analysis; Medical imagery; Tridimensional image; Random set; Graph method
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2012.06.002

► A novel brain MRI inhomogeneity correction method based on WM voxels. ► Automatic brain tissue segmentation by an optimum-path forest algorithm. ► Segmentation evaluation by delineation operating characteristic curve. ► Parameter independent and optimal comparison of brain tissue segmentation meth...