title: Improved watershed transform for medical image segmentation using prior information.
abstract: The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation.
authors.affiliation: Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02138, USA. vgrauc@bwh.harvard.edu
authors.forename: V
authors.initials: V
authors.lastname: Grau
authors.forename: A U J
authors.initials: AU
authors.lastname: Mewes
authors.forename: M
authors.initials: M
authors.lastname: Alcañiz
authors.forename: R
authors.initials: R
authors.lastname: Kikinis
authors.forename: S K
authors.initials: SK
authors.lastname: Warfield
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D002356:Cartilage / Q000033:anatomy & histology*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007719:Knee Joint / Q000033:anatomy & histology; D008279:Magnetic Resonance Imaging / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique