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Abstract #3980

Automatic Segmentation of Lung Parenchyma Using Fuzzy Clustering

Andr Fischer1, Christian Oliver Ritter1, Dietbert Hahn1, Herbert Kstler1

1Institute of Radiology, University of Wuerzburg, Wuerzburg, Germany

This work describes the ability of Fuzzy C-Means (FCM) clustering to accurately distinguish between pulmonary parenchyma, pulmonary vessels, the heart, and the surrounding tissue in dynamic contrast enhanced (DCE)-MRI. FCM clustering achieves this by clustering voxels with similar temporal signal courses together. A 3D DCE-MRI dataset was accordingly segmented and is presented in this work. This technique enables user independent automatic segmentation of the lung parenchyma necessary to quantify lung perfusion. Thereby, a subjective bias in data analysis as often present in manual parenchyma segmentation is lowered.