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Automated algorithm for the identification of joint space and phalanx margin locations on digitized hand radiographs
Author(s) -
Duryea J.,
Jiang Y.,
Countryman P.,
Genant H. K.
Publication year - 1999
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598537
Subject(s) - radiography , phalanx , interphalangeal joint , medicine , segmentation , algorithm , computer science , finger joint , artificial intelligence , computer vision , radiology , anatomy , surgery
Rheumatoid arthritis (RA) of the hand can be characterized and assessed by the narrowing of the joint spaces which are ordinarily scored semiquantitatively by a radiologist using radiographs of the hand. Software which delineates and measures the joint spaces would be a useful tool for assessment. The first part of such an algorithm has been developed which determines the locations of the distal interphalangeal (DIP), proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joint spaces for fingers 2–5 (index, middle, ring, and little) on digitized hand radiographs. In addition, points on the medial and lateral margins of each phalanx are identified which can be used as starting points for edge detection algorithms to provide segmentation of the phalanges. The algorithm is a C‐language program running on a UNIX computer, uses a multiresolution approach operating, and requires approximately 10 CPU seconds per image. It was tested on a set of 54 radiographs taken from a multicenter rheumatoid arthritis study where a study protocol was followed. In addition, radiographs of individuals wearing rings and where nonanatomical structures contacted the anatomy proximal to the midpoint of the distal phalanx and distal to the MCP joint were eliminated from the data set. In order to make a quantitative assessment, regions of interest drawn by a trained radiologist were used as a gold standard. The algorithm had a success rate of 100% for the identification of each digit and over 99% for the identification of joint space locations and phalanx margins. Quantitative tests indicated excellent algorithm robustness. We have developed fully automated software which accurately identifies anatomical landmarks on digital images of the hand.