ITAC volume assessment through a Gaussian hidden Markov random field model-based algorithm
| Autor: | Luca Mainardi, Paolo Potepan, Katia M. Passera, Luca Brambilla |
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| Rok vydání: | 2009 |
| Předmět: |
Models
Statistical Markov chain business.industry Gaussian Pattern recognition Adenocarcinoma Markov model Magnetic Resonance Imaging Markov Chains symbols.namesake Expectation–maximization algorithm symbols Humans Segmentation Artificial intelligence business Hidden Markov random field Hidden Markov model Spatial analysis Algorithm Algorithms Paranasal Sinus Neoplasms Mathematics |
| Zdroj: | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2008 |
| ISSN: | 2375-7477 |
| Popis: | In this paper, a semi-automatic segmentation method for volume assessment of Intestinal-type adenocarcinoma (ITAC) is presented and validated. The method is based on a Gaussian hidden Markov random field (GHMRF) model that represents an advanced version of a finite Gaussian mixture (FGM) model as it encodes spatial information through the mutual influences of neighboring sites. To fit the GHMRF model an expectation maximization (EM) algorithm is used. We applied the method to a magnetic resonance data sets (each of them composed by T1-weighted, Contrast Enhanced T1-weighted and T2-weighted images) for a total of 49 tumor-contained slices. We tested GHMRF performances with respect to FGM by both a numerical and a clinical evaluation. Results show that the proposed method has a higher accuracy in quantifying lesion area than FGM and it can be applied in the evaluation of tumor response to therapy. |
| Databáze: | OpenAIRE |
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