Learning shape and texture characteristics of CT tree-in-bud opacities for CAD systems

Authors:
Ulas Bagci;Jianhua Yao;Jesus Caban;Anthony F. Suffredini;Tara N. Palmore;Daniel J. Mollura
Affiliations:
Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD;Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD;National Library of Medicine, National Institutes of Health, Bethesda, MD;Critical Care Medicine Department, National Institutes of Health, Bethesda, MD;Laboratory of Clinical Infectious Diseases, National Institutes of Health, Bethesda, MD;Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

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 thickened bronchial structures surrounding by clusters of 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 Möbius 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%.