Automatic segmentation of lung fields on chest radiographic images.
In this work we have implemented a system for the automatic segmentation of lung fields in chest radiographic images. The image analysis process is carried out in three levels. In the first one we perform operations on the image that are independent from domain knowledge. This knowledge is implicitly and not very elaborately used in the intermediate level and used in an explicit manner in the high level block, globally corresponding to the idea of progressive segmentation. The representation of knowledge in the high level block is in the form of production rules. The control structure is in general bottom-up but there are certain hybrid control stages, in which the control is driven by the region model (main organs) we are seeking. We have applied the global system to a set of 45 posteroanterior (PA) chest radiographs, obtaining a mean degree of overlap with contours drawn by radiologists of 87%.
Carreira
M J
MJ
Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, E-15706, Spain.
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D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D008297:Male; D016571:Neural Networks, Computer; D011856:Radiographic Image Enhancement; D013902:Radiography, Thoracic