Automatic Kidney Segmentation Using Gaussian Mixture Model on MRI Sequences

  • Evgin Goceri
Conference paper

DOI: 10.1007/978-3-642-21747-0_4

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 99)
Cite this paper as:
Goceri E. (2011) Automatic Kidney Segmentation Using Gaussian Mixture Model on MRI Sequences. In: Wan X. (eds) Electrical Power Systems and Computers. Lecture Notes in Electrical Engineering, vol 99. Springer, Berlin, Heidelberg

Abstract

Robust kidney segmentation from MR images is a very difficult task due to the especially gray level similarity of adjacent organs, partial volume effects and injection of contrast media. In addition to different image characteristics with different MR scanners, the variations of the kidney shapes, gray levels and positions make the identification and segmentation task even harder. In this paper, we propose an automatic kidney segmentation approach using Gaussian mixture model (GMM) that adapts all parameters according to each MR image dataset to handle all these challenging problems. The efficiency in terms of the segmentation performance is achieved by the estimation of the GMM parameters using the Expectation Maximization (EM) method. The segmentation approach is compared to k-means method. The results show that the model based probabilistic segmentation technique gives better performance for both low contrast images and atypical kidney shapes where several algorithms fail on abdominal MR images.

Keywords

Kidney segmentation MRI Gaussian mixture model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Evgin Goceri
    • 1
  1. 1.Department of Computer Engineering, Faculty of EngineeringPamukkale UniversityDenizliTurkey

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