Title :
Atlas based AAM and SVM model for fully automatic MRI prostate segmentation
Author :
Ruida Cheng ; Turkbey, Baris ; Gandler, William ; Agarwal, H.K. ; Shah, Vijay P. ; Bokinsky, Alexandra ; McCreedy, Evan ; Wang, Shuhui ; Sankineni, Sandeep ; Bernardo, Marcelino ; Pohida, Thomas ; Choyke, Peter ; McAuliffe, Matthew J.
Author_Institution :
Image Sci. Lab., Nat. Inst. of Health, Bethesda, MD, USA
Abstract :
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%.
Keywords :
biological organs; biomedical MRI; edge detection; image segmentation; image texture; medical image processing; support vector machines; unsupervised learning; atlas based AAM model; atlas based SVM model; fully automatic MRI prostate boundary segmentation; interpatient prostate shape variability; interpatient prostate texture variability; supervised learning framework; Active appearance model; Image segmentation; Magnetic resonance imaging; Shape; Solid modeling; Support vector machines; Training;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6944225
