By Topic

An automated three-dimensional visualization and classification of emphysema using neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Tan Kok Liang ; Dept. of Appl. Phys. & Physico-Inf., Keio Univ., Yokohama ; Tanaka, T. ; Nakamura, H. ; Shirahata, T.
more authors

Chronic obstructive pulmonary disease (COPD) is a disease in which the airways and tiny air sacs (alveoli) inside the lungs are partially obstructed or destroyed. Emphysema is what occurs as more and more of the walls between air sacs get destroyed. Computed tomography (CT) image has been a useful modality for assessing diffuse lung diseases, particularly, emphysema. At present, diagnosis of emphysema is done by using spirometry, X-rays, spiral chest computed tomography (CT)-scan, bronchoscopy, blood tests and pulse oximetry. In this study, we extracted the two-dimensional emphysematous lung tissues in the lung CT automatically using digital image processing techniques, then we visualized the extracted emphysematous lung tissues by implementing a three-dimensional (3D) lung model which was computed using 55 pre-processed CT images, and finally we divided the lung model into eight sub-volumes and classified each sub-volume into five classes of emphysema related severity using an artificial neural network. The performance of the classifier was assessed using the leave-one-out method on 120 sub-volumes of the lungs generated from 15 COPD-verified patients' CT data sets.

Published in:

Signals, Systems and Computers, 2008 42nd Asilomar Conference on

Date of Conference:

26-29 Oct. 2008