Detecting osteophytes in radiographs of the knee to diagnose Osteoarthritis

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

Abstract

We present a fully automatic system for identifying osteophytes on knee radiographs, and for estimating the widely used Kellgren-Lawrence (KL) grade for Osteoarthritis (OA). We have compared three advanced modelling and texture techniques. We found that a Random Forest trained using Haar-features achieved good results, but the optimal results are obtained by combining shape modelling and texture features. The system achieves the best reported performance for identifying osteophytes (AUC: 0.85), for measuring KL grades and for classifying OA (AUC: 0.93), with an error rate half that of the previous best method.

Bibliographical metadata

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging
Subtitle of host publication7th International workshop, MLMI 2016 held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings
EditorsLi Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk
Place of PublicationSwitzerland
PublisherSpringer Nature
Pages45-52
Number of pages8
ISBN (Print)9783319471563
DOIs
Publication statusPublished - 1 Oct 2016

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer
Volume10019

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