Automatic prostate segmentation in MR images is a challenging task due to inter-patient prostate shape and texture variability, and the lack of a clear prostate boundary. We propose a supervised learning framework that combines the atlas based AAM and SVM model to achieve a relatively high segmentation result of the prostate boundary. The performance of the segmentation is evaluated with cross validation on 40 MR image datasets, yielding an average segmentation accuracy near 90%.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748308 | PMC |
| http://dx.doi.org/10.1109/EMBC.2014.6944225 | DOI Listing |
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