By Topic

A Linear Discriminant Analysis framework for bacterial type identification based on morphological parameters

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

2 Author(s)
S. Prabakar ; Dept. of EEE, Dr.N.G.P. Institute of Technology, Coimbatore-48. India ; K. Porkumaran

The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and identify the bacterial types. For feature selection of Bacterial identification system, the most successful method seems to be the appearance-based approach, which generally operates directly on images or appearances of bacterial objects. The image segmentation, Linear Discriminant Analysis (LDA), Direct Fractional LDA (DFLDA) and Principal Component Analysis (PCA) are the powerful tools used for feature extraction. Then the principal components are analyzed by DFLDA and simple Nearest Neighbor Classifier technique is used to identify the type of bacteria. The trained feed forward back propagation neural network is used for validating and testing the bacterial images. The effectiveness of the proposed method has been verified through experimentation using fifty popular bacterial image databases.

Published in:

Signal and Image Processing (ICSIP), 2010 International Conference on

Date of Conference:

15-17 Dec. 2010