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Journal of Electronic Imaging • Open Access

Tumor segmentation from breast magnetic resonance images using independent component texture analysis
Author(s): Sheng-Chih Yang; Chieh-Ling Huang; Tsai-Rong Chang; Chi-Yuan Lin

Paper Abstract

A new spectral signature analysis method for tumor segmentation in breast magnetic resonance images is presented. The proposed method is called an independent component texture analysis (ICTA), which consists of three techniques including independent component analysis (ICA), entropy-based thresholding, and texture feature registration (TFR). ICTA was mainly developed to resolve the inconsistency in the results of independent components (ICs) due to the random initial projection vector of ICA and then accordingly determine the most likely IC. A series of experiments were conducted to compare and evaluate ICTA with principal component texture analysis, traditional ICA, traditional principal component analysis (PCA), fuzzy c-means, constrained energy minimization, and orthogonal subspace projection methods. The experimental results showed that ICTA had higher efficiency than existing methods.

Paper Details

Date Published: 20 June 2013
PDF: 12 pages
J. Electron. Imaging. 22(2) 023027 doi: 10.1117/1.JEI.22.2.023027
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Sheng-Chih Yang, National Chin-Yi Univ. of Technology (Taiwan)
Chieh-Ling Huang, Chang Jung Christian Univ. (Taiwan)
Tsai-Rong Chang, Southern Taiwan Univ. of Science & Technology (Taiwan)
Chi-Yuan Lin, National Chin-Yi Univ. of Technology (Taiwan)


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