3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function

  • Fahmi Khalifa
  • Ahmed Elnakib
  • Garth M. Beache
  • Georgy Gimel’farb
  • Mohamed Abo El-Ghar
  • Rosemary Ouseph
  • Guela Sokhadze
  • Samantha Manning
  • Patrick McClure
  • Ayman El-Baz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Kidney segmentation is a key step in developing any non-invasive computer-aided diagnosis (CAD) system for early detection of acute renal rejection. This paper describes a new 3-D segmentation approach for the kidney from computed tomography (CT) images. The kidney borders are segmented from the surrounding abdominal tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated on 21 CT data sets with available manual expert segmentation. The performance evaluation based on the receiver operating characteristic (ROC) and Dice similarity coefficient (DSC) between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed segmentation approach.

Keywords

Compute Tomography Image Segmentation Approach Segmentation Accuracy Volumetric Error Segmentation Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fahmi Khalifa
    • 1
  • Ahmed Elnakib
    • 1
  • Garth M. Beache
    • 2
  • Georgy Gimel’farb
    • 3
  • Mohamed Abo El-Ghar
    • 4
  • Rosemary Ouseph
    • 5
  • Guela Sokhadze
    • 1
  • Samantha Manning
    • 1
  • Patrick McClure
    • 1
  • Ayman El-Baz
    • 1
  1. 1.BioImaging Laboratory, Bioengineering DepartmentUniversity of LouisvilleLouisvilleUSA
  2. 2.Diagnostic Radiology DepartmentUniversity of LouisvilleLouisvilleUSA
  3. 3.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  4. 4.Urology and Nephrology DepartmentUniversity of MansouraMansouraEgypt
  5. 5.Department of MedicineUniversity of LouisvilleLouisvilleUSA

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