Automated shape and texture analysis for detection of Osteoarthritis from radiographs of the knee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Osteoarthritis (OA) is considered to be one of the leading causes of disability, however clinical detection relies heavily on subjective experience to condense the continuous features into discrete grades. We present a fully automated method to standardise the measurement of OA features in the knee used to diagnose disease grade. Our approach combines features derived from both bone shape (obtained from an automated bone segmentation system) and image texture in the tibia. A simple weighted sum of the outputs of two Random Forest classifiers (one trained on shape features, the other on texture features) is sufficient to improve performance over either method on its own. We also demonstrate that Random Forests trained on simple pixel ratio features are as effective as the best previously reported texture measures on this task. We demonstrate the performance of the system on 500 knee radiographs from the OAI study.

Bibliographical metadata

Original languageEnglish
Title of host publicationProc. MICCAI 2015 Part 2
PublisherSpringer Nature
Pages127-135
Number of pages9
Publication statusPublished - Oct 2015
EventMedical Image Computing and Computer Assisted Intervention -
Event duration: 1 Jan 1824 → …

Conference

ConferenceMedical Image Computing and Computer Assisted Intervention
Period1/01/24 → …

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