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Wavelet Based Segmentation Using Optimal Statistical Features on Breast Images


Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India
2 PSN College of Engineering and Technology, India
     

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Elastography is the emerging imaging modality that analyzes the stiffness of the tissue for detecting and classifying breast tumors. Computer-aided detection speeds up the diagnostic process of breast cancer improving the survival rate. A multi resolution approach using Discrete wavelet transform is employed on real time images, using the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands of Daubechies family. Features are extracted, selected and then finally segmented by K-means clustering algorithm. The proposed work can be extended to Classification of the tumors.

Keywords

Daubechies Wavelet, Feature Selection, SFFS, K-Means.
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  • Wavelet Based Segmentation Using Optimal Statistical Features on Breast Images

Abstract Views: 250  |  PDF Views: 0

Authors

A. Sindhuja
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India
V. Sadasivam
PSN College of Engineering and Technology, India

Abstract


Elastography is the emerging imaging modality that analyzes the stiffness of the tissue for detecting and classifying breast tumors. Computer-aided detection speeds up the diagnostic process of breast cancer improving the survival rate. A multi resolution approach using Discrete wavelet transform is employed on real time images, using the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands of Daubechies family. Features are extracted, selected and then finally segmented by K-means clustering algorithm. The proposed work can be extended to Classification of the tumors.

Keywords


Daubechies Wavelet, Feature Selection, SFFS, K-Means.