Perthes Disease Classification Using Shape and Appearance Modelling

Adrian Davison, Timothy Cootes, Daniel C. Perry, Weisang Luo, Claudia Lindner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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We propose to use statistical shape and appearance modelling to classify the proximal femur in hip radiographs of children into Legg-Calvé-Perthes disease and healthy. Legg-Calvé-Perthes disease affects the femoral head with avascular necrosis, which causes large shape deformities during the growth-stage of the child. Further, the dead or dying bone of the femoral head is prominent visually in radiographic images, leading to a distinction between healthy bone and bone where necrosis is present. Currently, there is little to no research into analysing
the shape and appearance of hips affected by Perthes disease from radiographic images. Our research demonstrates how the radiographic shape, texture and overall appearance of a proximal femur affected by Perthes disease differs and how this can be used for identifying cases with the disease. Moreover, we present a radiograph-based fully automatic Perthes classification system that achieves state-of-the-art results with an area under the receiver operator characteristic (ROC) curve of 98%.
Original languageEnglish
Title of host publication6th MICCAI Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging
PublisherSpringer Nature
Volume11404 LNCS
Publication statusPublished - 2019
EventComputational Methods and Clinical Applications in Musculoskeletal Imaging: 6th Workshop & Challenge in conjunction with MICCAI 2018 - Granada, Spain
Duration: 16 Sept 201816 Sept 2018

Publication series

NameLecture Notes in Computer Science


WorkshopComputational Methods and Clinical Applications in Musculoskeletal Imaging
Abbreviated titleMICCAI-MSKI2018
Internet address


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