Liver segmentation using location and intensity probabilistic atlases
Negar Farzaneh, Shadrokh Samavi, S. M. Reza Soroushmehr, Hirenkumar Patel, Samuel Habbo-Gavin, David Paul Fessell, Kevin R. Ward, Kayvan Najarian · 2016
In a variety of injuries and illnesses, internal organs in the abdominal and pelvic regions, in particular liver, may be compromised. In the current practice, CT scans of liver are visually inspected to investigate the integrity of the organ. However, the size and complexity of the CT images limits the reliability of visual inspection to accurately assess the health of liver. Computer-aided image analysis can create fast and quantitative assessment of liver from the CT, in particular in the environments where access to skilled radiologists may be limited. In this paper we propose a hierarchical method based on probabilistic models of position and intensity of voxels for automated segmentation of liver that achieves the Dice similarity coefficient of higher than 89%.