By Topic

Automated Feature Selection for Pathogen Yeast Cryptococcus Neoformans

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

5 Author(s)
Jinshuo Liu ; Computer School, Wuhan University, Wuhan, P.R.China. Email: jsureliu@gmail.com ; Dengyi Zhang ; Yu Yao ; Shubo Liu
more authors

Due to large storage of images, it is highly requested to analyze images in a fast and efficient way. Data mining and pattern recognition methods have been widely used to understand the image knowledge deeply inside. Feature selection and extraction is the preprocessing step of data mining. Our approach to mine from Images, deals mainly with identification and extraction of unique features for analysing the pathogen conditions of yeast Cryptococcus Neoformans. Our automated model can determine which features can be used to identify variance pathogen condition. Different methods for extraction have been tried. Features extracted and techniques used are evaluated using the new test set images. Experimental results show that the features extracted by our automated data driven model are sufficient to identify the patterns from the images.

Published in:

2007 IEEE International Symposium on Industrial Electronics

Date of Conference:

4-7 June 2007