Thyroid Texture Representation via Noise Resistant Image Features

Authors:
Eystratios G. Keramidas;Dimitris K. Iakovidis;Dimitris Maroulis;Nikos Dimitropoulos
Affiliations:
-;-;-;-
Venue:
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Year:
2008

Citing 0
Cited 5

Local binary patterns variants as texture descriptors for medical image analysis

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Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns

Artificial Intelligence in Medicine
Combining different local binary pattern variants to boost performance

Expert Systems with Applications: An International Journal
Survey on LBP based texture descriptors for image classification

Expert Systems with Applications: An International Journal
A simple method for improving local binary patterns by considering non-uniform patterns

Pattern Recognition

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Abstract

The robustness of textural features on speckle noise is of vital importance for ultrasound imaging. A set of novel fuzzy features for thyroid ultrasound texture representation, demonstrating noise-resistant properties, is presented, analyzed and evaluated in this study. The textural feature extraction scheme is based on the fuzzyfication of the local binary pattern approach. The proposed features are evaluated on an annotated dataset of B-mode thyroid ultrasound images acquired from 75 patients. The experimental results illustrate that these features provide accurate representation of the thyroid texture. They can be effectively utilized for thyroid nodule detection outperforming other thyroid texture representation approaches that have been recently proposed in the literature.