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Separable and Non-Separable Discrete Wavelet Transform Based Texture Features and Image Classification of Breast Thermograms Publisher



Etehadtavakol M1 ; Ng EYK2 ; Chandran V3 ; Rabbani H1, 4
Authors
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Authors Affiliations
  1. 1. Medical Image and Signal Processing Research Centre, Isfahan University of Medical Sciences, Isfahan 81745-319, Iran
  2. 2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, 50 Nanyang Avenue, Singapore
  3. 3. School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD 4001, Australia
  4. 4. Department of Physics and Biomedical Engineering, Isfahan University of Medical Sciences, 81465-1148 Isfahan, Iran

Source: Infrared Physics and Technology Published:2013


Abstract

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts. © 2013 Published by Elsevier B.V.
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