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Author Notes:

bfei@emory.edu, Website: http://feilab.org


Research Funding:

This research is supported in part by NIH grant R01CA156775 (PI: Fei), Coulter Translational Research Grant (PIs: Fei and Hu), Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), Emory Molecular and Translational Imaging Center (NIH P50CA128301), and Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by the PHS Grant UL1 RR025008 from the Clinical and Translational Science Award program.


  • Kidney
  • image segmentation
  • Wavelet
  • Support Vector Machines
  • MRI
  • Probability Shape Model
  • Polycystic kidney disease

Automatic 3D Segmentation of the Kidney in MR Images Using Wavelet Feature Extraction and Probability Shape Model


Journal Title:

Proceedings of SPIE


Volume 8314


, Pages 8314D-8314D

Type of Work:

Article | Post-print: After Peer Review


Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet-based support vector machines (W-SVMs) is trained on the MR images. The W-SVMs capture texture priors of MRI for classification of the kidney and non-kidney tissues in different zones around the kidney boundary. In the segmentation procedure, these W-SVMs are trained to tentatively label each voxel around the kidney model as a kidney or non-kidney voxel by texture matching. A probability kidney model is created using 10 segmented MRI data. The model is initially localized based on the intensity profiles in three directions. The weight functions are defined for each labeled voxel for each Wavelet-based, intensity-based, and model-based label. Consequently, each voxel has three labels and three weights for the Wavelet feature, intensity, and probability model. Using a 3D edge detection method, the model is re-localized and the segmented kidney is modified based on a region growing method in the model region. The probability model is re-localized based on the results and this loop continues until the segmentation converges. Experimental results with mouse MRI data show the good performance of the proposed method in segmenting the kidney in MR images.

Copyright information:

© (2012) Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

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