By Topic

Texture feature extraction and description using gabor wavelet in content-based medical image retrieval

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

2 Author(s)
Gang Zhang ; Northeastern Univ., Shenyang ; Ma Zong-min

Gabor wavelet is one of the important methods for texture feature extraction and description in content-based medical image retrieval. Usually Gabor wavelet is used for a special scale set and a special direction set. However, this usually cannot extract the most discriminative texture features. In this paper, a new method for texture feature extraction and description is proposed. The method starts from whole scale space and whole direction space, and extracts time-frequency coefficients from each scale and each direction using Gabor wavelet. The energy is computed according to the coefficients, and dominant multi-scale and multi-direction fuzzy set is computed based on all energy computed. The standardized energy is used to measure the dominance of each element. Texture feature vector is computed according to the fuzzy set. The similarity measure is carried out between the fuzzy sets. When two images are of the same kind, their similarity measure is carried out between the texture feature vectors of two images. The experiments show that the method prosed in the paper has good retrieval performances for normal medical images.

Published in:

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on  (Volume:1 )

Date of Conference:

2-4 Nov. 2007