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AB0743 A hand positioning frame improves joint recognition on hand radiographs
  1. O. Schenk1,
  2. J. A. Kauffman1,
  3. C. H. Slump1,
  4. H. J. Bernelot Moens2
  1. 1Signals and Systems Group, University of Twente, Enschede
  2. 2Department of Rheumatology, Hospital Group Twente, Hengelo, Netherlands

Abstract

Background Current clinical scoring methods for hand radiographs in RA are time consuming and subject to intra and inter-reader variance. Several methods for partially automatic radiographic assessment of hand radiographs were proposed [1]. These methods depend on detection (segmentation) of bones and joints. Their success rate is unpublished.

We developed a semi-automated pattern recognition method [2]. To model bone shape variation independent from hand positions, this method used connected submodels and an iterative search to find the bones of the hand. These models were combined into a single model of the entire hand. The wide variation in the position of hands on subsequent radiographs was an obstacle in joint recognition and measurement of joint space width. We have therefore developed a positioning frame with 7 pins to reduce positioning error.

Objectives Compare the success rate of joint recognition on hand radiographs made without and with a positioning aid.

Methods The positioning frame was introduced in 2011 for hand radiographs of RA patients. A random selection of 91 images made before, and 87 made after introduction of the frame was analyzed using the previous described model [2]. Radiographs made with the frame were analyzed with the same model, with the addition to check for correct detection of hands relative to the pins. Processed images are intended for measurements of joint damage and have bone outlines surrounded by boxes.

For 14 joints per hand (MCP, PIP, DIP) segmentation was judged by a rheumatologist and considered correct when at least part of the joint space was included in the overlapping boxes around the adjoining bones.

Conclusions The use of a hand positioning frame improves the performance of previously developed segmentation software for analysis of hand radiographs. This brings us closer to a fully automated system that measures joint damage. Training of the model using a wider range of hands may further improve segmentation.

  1. J.T.Sharp et al, Computer based methods for measurement of joints space width: update of an ongoing OMERACT project, Journal of Rheumatology, 34(4):874-883,2007

  2. J.A.Kauffman et al, Segmentation of hand radiographs by using multi-level connected active appearance models, Proceedings of Medical Imaging2005: Image Processing, 5747:1571-1581,2005

Disclosure of Interest None Declared

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