By Topic

The Use of a Slice Feature Vector of Classifying Diffuse Lung Opacities in High-Resolution Computed Tomography Images

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
$33 $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

4 Author(s)
Yoshihiro Mitani ; Intell. Syst. Eng., Ube Nat. Coll. of Technol., Ube, Japan ; Yusuke Fujita ; Naofumi Matsunaga ; Yoshihiko Hamamoto

The classification of diffuse lung opacities in high resolution computed tomography(HRCT) images is an important step for developing a computer-aided diagnosis(CAD) system. In designing the CAD system for classifying diffuse lung opacities in HRCT images, a histogram feature vector approach has been shown to be effective. In order to improve further the classification performance of the CAD system, we have explored another type of feature vector. Furthermore, the combination of these features may be used for lung opacities classification. In this paper, we have proposed the use of a slice feature vector. The experimental results show that the proposed method is promising.

Published in:

2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation

Date of Conference:

25-27 Sept. 2012