Share Email Print

Proceedings Paper

Multiscale shape features for classification of bronchovascular anatomy in CT using AdaBoost
Author(s): Robert A. Ochs; Jonathan G. Goldin; Fereidoun Abtin; Hyun J. Kim; Kathleen Brown; Poonam Batra; Donald Roback; Michael F. McNitt-Gray; Matthew S. Brown
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Lung CAD systems require the ability to classify a variety of pulmonary structures as part of the diagnostic process. The purpose of this work was to develop a methodology for fully automated voxel-by-voxel classification of airways, fissures, nodules, and vessels from chest CT images using a single feature set and classification method. Twenty-nine thin section CT scans were obtained from the Lung Image Database Consortium (LIDC). Multiple radiologists labeled voxels corresponding to the following structures: airways (trachea to 6th generation), major and minor lobar fissures, nodules, and vessels (hilum to peripheral), and normal lung parenchyma. The labeled data was used in conjunction with a supervised machine learning approach (AdaBoost) to train a set of ensemble classifiers. Each ensemble classifier was trained to detect voxels part of a specific structure (either airway, fissure, nodule, vessel, or parenchyma). The feature set consisted of voxel attenuation and a small number of features based on the eigenvalues of the Hessian matrix (used to differentiate structures by shape) computed at multiple smoothing scales to improve the detection of both large and small structures. When each ensemble classifier was composed of 20 weak classifiers, the AUC values for the airway, fissure, nodule, vessel, and parenchyma classifiers were 0.984 ± 0.011, 0.949 ± 0.009, 0.945 ± 0.018, 0.953 ± 0.016, and 0.931± 0.015 respectively.

Paper Details

Date Published: 3 March 2007
PDF: 11 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65120T (3 March 2007); doi: 10.1117/12.707655
Show Author Affiliations
Robert A. Ochs, Univ. of California/Los Angeles (United States)
Jonathan G. Goldin, Univ. of California/Los Angeles (United States)
Fereidoun Abtin, Univ. of California/Los Angeles (United States)
Hyun J. Kim, Univ. of California/Los Angeles (United States)
Kathleen Brown, Univ. of California/Los Angeles (United States)
Poonam Batra, Univ. of California/Los Angeles (United States)
Donald Roback, Univ. of California/Los Angeles (United States)
Michael F. McNitt-Gray, Univ. of California/Los Angeles (United States)
Matthew S. Brown, Univ. of California/Los Angeles (United States)

Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?