QUANTIFICATION OF PULMONARY-EMPHYSEMA FROM LUNG COMPUTED-TOMOGRAPHY IMAGES

A texture-based adaptive multiple feature method (AMFM) for evaluating pulmonary parenchyma from computed tomography (CT) images is describe d. This method incorporates multiple statistical and fractal texture f eatures. The AMFM was compared to two previously published methods, na mely, mean lung density (MLD) and the lowest fifth percentile of the h istogram (HIST). First, the ability of these methods to detect subtle differences in ventral-dorsal lung density gradient in the prone norma l rung was studied. Second, their abilities to differentiate between n ormal and emphysematous whole lung slices were compared, Finally, regi onal analyses comparing normal and emphysematous regions were performe d by dividing the lungs in the CT slices into six equal regions, ventr al to dorsal, and analyzing each region separately, The results demons trated that the AMFM could separate the ventral from the dorsal one-th ird of the normal prone lung with 89.8% accuracy, compared to an accur acy of 74.6% with the MLD and 64.4% with the HIST methods. The normal and emphysematous slices were separated on a global basis with 100.0% accuracy using the AMFM as compared to an accuracy of 94.7% and 97.4% using the MLD and HIST methods, respectively. The regional normal and emphysematous tissues were discriminated with an average accuracy of 9 7.9%, 89.9%, and 99.1% with the AMFM, MLD, and HIST methods, respectiv ely. The three methods and the pulmonary function tests in the normal and emphysema groups were poorly correlated. Quantitative texture anal ysis using adaptive multiple features holds promise for the objective noninvasive evaluation of the pulmonary parenchyma.