DocumentCode :
2721681
Title :
Relational statistical deformation models for morphological image analysis and classification
Author :
Caban, Jesus J. ; Rheingans, Penny
Author_Institution :
Nat. Libr. of Med., Nat. Institutes of Health, Bethesda, MD, USA
fYear :
2010
fDate :
14-17 April 2010
Firstpage :
1333
Lastpage :
1336
Abstract :
As medical imaging datasets continue to grow, interest in effective ways to analyze the statistical properties and data variability within those datasets has surged. Accurate analysis of the morphological statistical properties of a group of images has proven to be extremely important in medical imaging. This paper introduces Relational Statistical Deformation Models, or RSDMs, as a generic modeling technique to capture the morphological statistical deformation properties of a collection of images. Deformation fields, such as those obtained from non-linear registration techniques, are used to learn the morphological properties of a group of images and to train a statistical model capable of solving multiple imaging tasks such as image classification. To compensate for noise and registration errors, RSDM treats each local deformation as a random variable and builds the statistical model as a Markov Random Field (MRF). Once an RSDM model has been created, the same model can be used to solve multiple imaging tasks such as image classification, diagnosis, generation, and denoising. The focus of this paper is to introduce RSDM models and illustrate their effectiveness in image classification tasks. To show the advantages and limitations of RSDMs, a collection of brain MR images was used to create a model to automatically identify subjects with Alzheimer´s.
Keywords :
Markov processes; biomedical MRI; brain; deformation; diseases; image classification; image denoising; image registration; medical image processing; random processes; statistical analysis; Alzheimer disease; MRI; Markov random field; brain; denoising; image classification; morphological image analysis; nonlinear registration; random variable; relational statistical deformation models; Biomedical imaging; Deformable models; Focusing; Image analysis; Image classification; Image generation; Markov random fields; Noise reduction; Random variables; Surges; Alzheimers disease; Statistical model; classification; deformation models; morphological analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2010 IEEE International Symposium on
Conference_Location :
Rotterdam
ISSN :
1945-7928
Print_ISBN :
978-1-4244-4125-9
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2010.5490243
Filename :
5490243
Link To Document :
بازگشت