DocumentCode :
2630092
Title :
4-D lesion detection using expectation-maximization and hidden Markov model
Author :
Solomon, Jeffrey ; Sood, Arun
fYear :
2004
fDate :
15-18 April 2004
Firstpage :
125
Abstract :
We explored the use of spatial and temporal information to automatically detect lesions in 4D medical image data (3D space + 1D time). The 3D method, expectation-maximization (E-M), was used to estimate the probability distributions of various tissue classes in the image. Evolution of the lesion over time is assumed to follow a hidden Markov model (HMM), where the state of the system is expressed as lesion or non-lesion independently for each voxel in the image. Synthetic images based on a Gaussian mixture model were used to simulate a 4D image data set with exponential lesion growth. A comparison was made between use of the E-M algorithm alone, run independently on each image in the time series, and the E-M plus HMM temporal approach. The combination of techniques showed improvement in sensitivity and specificity of lesion detection.
Keywords :
Gaussian distribution; biological tissues; biomedical MRI; hidden Markov models; medical image processing; spatiotemporal phenomena; time series; 4-D lesion detection; 4D medical image; Gaussian mixture model; expectation-maximization method; exponential lesion growth; hidden Markov model; probability distributions; spatial information; temporal information; time series; Automation; Biomedical imaging; Cancer; Diseases; Hidden Markov models; Image segmentation; Lesions; Medical diagnostic imaging; Medical simulation; Shape;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on
Print_ISBN :
0-7803-8388-5
Type :
conf
DOI :
10.1109/ISBI.2004.1398490
Filename :
1398490
Link To Document :
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