title: Automatic detection of anatomical landmarks on the knee joint using MRI data.
abstract: OBJECTIVE
  To propose a new automated learning-based scheme for locating anatomical landmarks on the knee joint using three-dimensional (3D) MR image data.
  
  
  METHODS
  This method makes use of interest points as candidates for landmarks. All candidates are evaluated by a "coarse to fine" 3D feature descriptor computed from manually placed landmarks in training datasets. The results are refined using a multi-classifier boosting system. We demonstrate our method by the detection of 24 landmarks on the knee joint of 35 subjects. To verify the robustness, the test datasets differ in contrast, resolution, patient positioning, and health condition of the knee joint. The proposed method is evaluated by measuring the distance between manually placed landmarks and automatically detected landmarks and the computational cost for detecting one landmark in a 3D dataset.
  
  
  RESULTS
  The results reveal that the method is capable of localizing landmarks with a reasonable accuracy (1.64 ± 1.03 mm [mean ± standard deviation]), sensitivity (97%) and run time efficiency (4.82 s).
  
  
  CONCLUSIONS
  This study suggests that the proposed method is an accurate and robust approach for the automated landmark detection in various MR datasets. The proposed method can be used as the initialization or constraint in higher level medical image processing workflows such as in kinematic description, segmentation and registration.
authors.affiliation: Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Germany; Imaging and Therapy Division Healthcare Sector, Siemens AG, Erlangen, Germany.
authors.forename: Ning
authors.initials: N
authors.lastname: Xue
authors.forename: Michael
authors.initials: M
authors.lastname: Doellinger
authors.forename: Charles P
authors.initials: CP
authors.lastname: Ho
authors.forename: Rachel K
authors.initials: RK
authors.lastname: Surowiec
authors.forename: Raphael
authors.initials: R
authors.lastname: Schwarz
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007719:Knee Joint / Q000033:anatomy & histology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011871:Radiology / Q000193:education*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: boosting; feature description; interest points; landmark detection