Kidney segmentation using graph cuts and pixel connectivity

Authors:
Ashish K. Rudra;Ananda S. Chowdhury;Ahmed Elnakib;Fahmi Khalifa;Ahmed Soliman;Garth Beache;Ayman El-Baz
Affiliations:
Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA;Department of Diagnostic Radiology, School of Medicine, University of Louisville, Louisville, KY, USA;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA
Venue:
Pattern Recognition Letters
Year:
2013

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Abstract

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.