Towards quantification of interstitial pneumonia patterns in lung multidetector CT

Quantification of Diffuse Parenchyma Lung Disease (DPLD) patterns challenges Computer Aided Diagnosis schemes in Computed Tomography (CT) lung analysis. In this study, an automated scheme for volumetric quantification of Interstitial Pneumonia (IP) patterns, a subset of DPLDs, is presented, utilizing a MultiDetector CT (MDCT) data set. Initially, Lung Field (LF) segmentation is achieved by 3D automated gray level thresholding combined to wavelet highlighting, followed by a texture based border refinement step. The vessel tree volume is identified and removed from LF, resulting in Lung Parenchyma (LP) volume. Following, the abnormal LP is differentiated from normal LP utilizing a 2 class k-means clustering. Quantification of IP patterns is formulated as a three-class pattern recognition problem to classify abnormal LP into ground glass, reticular and honeycomb patterns, by means of SVM voxel classification, exploiting 3D co-occurrence features. Performance of the proposed scheme in segmenting LF, as well as in quantifying normal LP, ground glass, reticular and honeycomb patterns was evaluated by means of volume overlap on 5 MDCT scans. Volume overlap for left LF and right LF was 0.95±0.03 and 0.96±0.02 respectively, while for normal LP, ground glass, reticular and honeycombing patterns was 0.89±0.02, 0.70±0.04, 0.72±0.05 and 0.71±0.03, respectively.