  Although radiologists can employ CAD systems to characterize malignancies,
pulmonary fibrosis and other chronic diseases; the design of imaging techniques
to quantify infectious diseases continue to lag behind. There exists a need to
create more CAD systems capable of detecting and quantifying characteristic
patterns often seen in respiratory tract infections such as influenza,
bacterial pneumonia, or tuborculosis. One of such patterns is Tree-in-bud (TIB)
which presents \textit{thickened} bronchial structures surrounding by clusters
of \textit{micro-nodules}. Automatic detection of TIB patterns is a challenging
task because of their weak boundary, noisy appearance, and small lesion size.
In this paper, we present two novel methods for automatically detecting TIB
patterns: (1) a fast localization of candidate patterns using information from
local scale of the images, and (2) a M\"{o}bius invariant feature extraction
method based on learned local shape and texture properties. A comparative
evaluation of the proposed methods is presented with a dataset of 39 laboratory
confirmed viral bronchiolitis human parainfluenza (HPIV) CTs and 21 normal lung
CTs. Experimental results demonstrate that the proposed CAD system can achieve
high detection rate with an overall accuracy of 90.96%.
