ICIP 2006, Atlanta, GA

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Paper Detail

Session:Biomedical Image Segmentation
Time:Monday, October 9, 12:00 - 12:20
Presentation: Lecture
Topic: Biomedical Imaging: Biomedical image segmentation and quantitative analysis
Authors: Gady Agam; Illinois Institute of Technology 
 Daniel Weiss; Illinois Institute of Technology 
 Mandar Soman; Illinois Institute of Technology 
 Konstantinos Arfanakis; Illinois Institute of Technology 
Abstract: Lesion segmentation in MRI scans is used for lesion quantification as pertaining to various medical conditions. We propose a novel technique for chronic stroke lesion segmentation based on multiple modalities including T1-weighted and T2-weighted images as well as diffusion tensor-based modalities. The proposed approach is based on a mixture-parametric probabilistic model whereas the model parameters are optimized by maximizing the incomplete-data log-likelihood function through expectation maximization. The mixture components are selected to have Cauchy distributions thus facilitating efficient computation and increased robustness to noise. A probabilistic prior is computed by evaluating the feature vectors for a set of registered brain scans in a control set. Experimental results on actual clinical data demonstrate the effectiveness of the proposed approach.