By Topic

Automatic Classification of Focal Lesions in Ultrasound Liver Images using Principal Component Analysis and Neural Networks

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

3 Author(s)

Ultrasound Medical Imaging is currently the most popular modality for diagnostic application. This imaging technique has been used for the detecting abnormalities associated with abdominal organs like liver, kidney, uterus etc. In this paper, the possibilities of automatic classification of the ultrasound liver images into four classes-normal, cyst, benign and malignant masses, using texture features are explored. These texture features are extracted using the various statistical and spectral methods. The optimal feature selection process is carried out manually to pick the best discriminating features from the extracted texture parameters. Also, the method of principal component analysis is used to extract the principal features or directions of maximum information from the data set there by automatically selecting the optimal features. Using these optimal features, a final combined feature set is formed and is employed for classification of the liver lesions into respective classes. K-means clustering and neural network based automatic classifiers are employed in this process. The classifier design and its performance are studied. This paper summarizes the various statistical and spectral texture parameter extraction processes, optimal feature selection techniques and automated classification procedures involved in our work.

Published in:

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Date of Conference:

22-26 Aug. 2007