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Abstract #2686

Segmentation of Kidney Cortex and Medulla on MR Images by Use of Multi-Feature K-Means Method

Yin Huang1, Deborah Yagow2, Nathan Artz1, Elizabeth Sadowski3, Arjang Djamali4, Sean Fain1

1Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; 2Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States; 3Radiology, University of Wisconsin-Madison, Madison, WI, United States; 4Nephrology, University of Wisconsin-Madison, Madison, WI, United States


The K-means segmentation method was implemented to automatically segment kidney cortex and medulla on MR images of 24 subjects based on two kidney feature values -- T1 and perfusion weighted information. Manual segmentation results on the same subjects were used as reference and three similarity measures were calculated to evaluate the effectiveness of K-means segmentation. The segmentation time was radically shortened by K-means compared with manual operation. However, there are about 30% of all subjects that K-means segmentation did not work well so that a semi-automated strategy can be suggested to incorporate manual segmentation when necessary.