Abnormal nodular branching opacities at the lung periphery in Chest Computed Tomography (CT) are termed by radiology literature as tree-in-bud (TIB) opacities. These subtle opacity differences represent pulmonary disease in the small airways such as infectious or inflammatory bronchiolitis. Precisely quantifying the detection and measurement of TIB abnormality using computer assisted detection (CAD) would assist clinical and research investigation of this pathology commonly seen in pulmonary infections. This paper presents a novel method for automatically detecting TIB patterns based on fast localization of candidates using local scale information of the images. The proposed method combines shape index, local gradient statistics, and steerable wavelet features to automatically identify TIB patterns. Experimental results using 39 viral bronchiolitis human para-influenza (HPIV) CTs and 21 normal lung CTs achieved an overall accuracy of 89.95%.