Kidney segmentation using graph cuts and pixel connectivity

• Segmentation of kidney from extremely low contrast abdominal MRI data.
• Segmentation achieved using improved graph cuts through pixel labeling.
• Pixel labeling done following Dijkstra’s shortest path algorithm.
• Fast yet highly accurate kidney segmentation strategy.

Kidney segmentation from abdominal MRI data is used as an effective and accurate indicator for renal function in many clinical situations. The goal of this research is to accurately segment kidney from very low contrast MRI data. The present problem becomes challenging mainly due to poor contrast, high noise and partial volume effects introduced during the scanning process. In this paper, we propose a novel kidney segmentation algorithm using graph cuts and pixel connectivity. A connectivity term is introduced in the energy function of the standard graph cut via pixel labeling. Each pixel is assigned a different label based on its probabilities to belong to two different segmentation classes and probabilities of its neighbors to belong to these segmentation classes. The labeling process is formulated according to Dijkstra’s shortest path algorithm. Experimental results yield a (mean ± s.d.) Dice coefficient value of (98.60 ± 0.52)% on 25 datasets.