title: Atlas based AAM and SVM model for fully automatic MRI prostate segmentation.
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%.
authors.forename: Ruida
authors.initials: R
authors.lastname: Cheng
authors.forename: Baris
authors.initials: B
authors.lastname: Turkbey
authors.forename: William
authors.initials: W
authors.lastname: Gandler
authors.forename: Harsh K
authors.initials: HK
authors.lastname: Agarwal
authors.forename: Vijay P
authors.initials: VP
authors.lastname: Shah
authors.forename: Alexandra
authors.initials: A
authors.lastname: Bokinsky
authors.forename: Evan
authors.initials: E
authors.lastname: McCreedy
authors.forename: Shijun
authors.initials: S
authors.lastname: Wang
authors.forename: Sandeep
authors.initials: S
authors.lastname: Sankineni
authors.forename: Marcelino
authors.initials: M
authors.lastname: Bernardo
authors.forename: Thomas
authors.initials: T
authors.lastname: Pohida
authors.forename: Peter
authors.initials: P
authors.lastname: Choyke
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: McAuliffe
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis*; D015203:Reproducibility of Results; D060388:Support Vector Machine