title: Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.
abstract: Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.
authors.affiliation: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA.
authors.forename: Mahesh B
authors.initials: MB
authors.lastname: Nagarajan
authors.affiliation: Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany; Faculty of Physics, Ludwig Maximilian University, Munich, Germany; European Synchrotron Radiation Facility, Grenoble, France.
authors.forename: Paola
authors.initials: P
authors.lastname: Coan
authors.affiliation: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA.
authors.forename: Markus B
authors.initials: MB
authors.lastname: Huber
authors.affiliation: Faculty of Physics, Ludwig Maximilian University, Munich, Germany; European Synchrotron Radiation Facility, Grenoble, France.
authors.forename: Paul C
authors.initials: PC
authors.lastname: Diemoz
authors.affiliation: Departments of Imaging Sciences and Biomedical Engineering, University of Rochester Medical Center, Rochester, New York, USA; Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University, Munich, Germany.
authors.forename: Axel
authors.initials: A
authors.lastname: Wismüller
mesh_terms: D000328:Adult; D019540:Area Under Curve; D002356:Cartilage / Q000000981:diagnostic imaging*; D006801:Humans; D010003:Osteoarthritis / Q000145:classification / Q000000981:diagnostic imaging; D010329:Patella; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed