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Automated lung segmentation in digital posteroanterior and lateral chest radiographs: Applications in diagnostic radiology and nuclear medicine
Author(s) -
Armato Samuel G.
Publication year - 1997
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 - 10.1118/1.598137
Subject(s) - radiography , digital radiography , thresholding , medicine , radiology , computed radiography , segmentation , cad , medical imaging , nuclear medicine , computer aided diagnosis , artificial intelligence , computer science , image quality , engineering drawing , engineering , image (mathematics)
Automated segmentation of anatomic regions in digital radiographs is a key element of computer‐aided diagnostic (CAD) methods. Computerized methods for automatically segmenting the aerated lung fields from digital posteroanterior (PA) and lateral chest radiographs have been developed. The methods are based on gray‐level thresholding techniques, including iterative global gray‐level thresholding and local gray‐level thresholding, thus reducing the need for a priori assumptions regarding overall lung morphology. Consequently, this method is particularly applicable to the detection of abnormal asymmetry in PA radiographs, where pathological processes may produce gross alterations in the appearance of radiographic lung anatomy. An anatomic region of particular interest is the costophrenic angle. The method described lends itself to the automated assessment of costophrenic angle blunting, for which a technique has been developed. Another application of this method crosses into the field of nuclear medicine. Radionuclide lung scans are typically used in conjunction with chest radiographs for diagnostic purposes. An automated method for registering digital PA and lateral chest radiographs with the perfusion and ventilation images of a lung scan has been developed. These computerized techniques may be incorporated into future CAD schemes and may potentially aid radiologists in the interpretation and assessment of medical images.