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Image feature analysis and computer‐aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields
Author(s) -
Giger Maryellen Lissak,
Doi Kunio,
MacMahon Heber
Publication year - 1988
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 - 10.1118/1.596247
Subject(s) - feature (linguistics) , feature extraction , digital radiography , radiography , lung , radiology , artificial intelligence , computer aided diagnosis , computer science , computed radiography , pattern recognition (psychology) , computer vision , medicine , image (mathematics) , image quality , philosophy , linguistics
We are investigating the characteristic features of lung nodules and the surrounding normal anatomic background in order to develop an algorithm of computer vision for use as an aid in the detection of nodules in digital chest radiographs. Our technique involves an attempt to eliminate the background anatomic structures in the lung fields by means of a difference image approach. Then, feature‐extraction techniques, such as tests for circularity, size, and their variation with threshold level, are applied so that suspected nodules can be isolated. Preliminary results of this automated detection scheme yielded high true‐positive rates and low false‐positive rates in the peripheral lung regions of the chest. This detection scheme, which can assist the final diagnosis by the clinician, has the potential to improve the early detection of lung carcinomas.