A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection

M. Martin-Fernandez, C. Alberola-Lopez
MICCAI, Tokyo, Japan
Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02), Lecture Notes in Computer Science 2489
Pages 397-404
September 25-28, 2002

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Abstract

Automatic detection of structures in medical images is of great importance for the implementation of tools that can obtain accurate measurements for an eventual diagnosis. In this paper, a new method for the creation of such tools is presented. We focus on in vivo kidney ultrasound, a target in which classical methods fail due to the inherent difficulty of such an imaging modality and organ. The proposed method operates on every slice by detecting kidney contours under a probabilistic Bayesian framework. We make use of Markov Random Fields ideas to model the problem and find the solution. A computer easy-to-use interface to the model is also presented.

Reference

Martin-Fernandez M, Alberola-Lopez C. A bayesian approach to in vivo kidney ultrasound contour detection. In T Dohi, R Kikinis, eds., Fifth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'02), Lecture Notes in Computer Science 2489. Tokyo, Japan: Springer Verlag, 2002;397-404.

Bibtex entry

@InProceedings{martin-fernandezMICCAI02,
  author         = {M. Martin-Fernandez and C. Alberola-Lopez},                
  title          = {A Bayesian Approach to in vivo Kidney Ultrasound Contour   
                   Detection},                                                 
  editor         = {T. Dohi and R. Kikinis},                                   
  booktitle      = {Fifth International Conference on Medical Image Computing  
                   and  Computer-Assisted Intervention (MICCAI'02), Lecture    
                   Notes in Computer Science 2489},                            
  pages          = {397--404},                                                 
  year           = {2002},                                                     
  address        = {Tokyo, Japan},                                             
  month          = {September 25--28},                                         
  publisher      = {Springer Verlag},                                          
  note           = {}
}

Grants

NIH P41-RR13218 (NAC), CIMIT

Research areas

DTMRI, Tensor

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