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Improved Spatial Gray Level Dependence Matrices for Texture Analysis


Affiliations
1 Advanced Technology for Medicine and Signals ATMS, ENIS, University of Sfax, Tunisia
 

In this paper, we will focus on the Spatial Gray Level Dependence Matrices SGLDM to extract the Haralick's texture features of the ultrasound breast lesions. This method relies on the manual selection of the region of interest, which results in the dependence of parameters values on the extracted region. For that reason, an improved Spatial Gray Level Dependence Matrices based on the segmented masses using active contour was applied. This method outperforms the existing SGLDM method because it allows establishing a well determined threshold for the classification of lesions.

Keywords

Texture Analysis, Co-Occurrence Matrix, Spatial Gray Level Dependence Matrices, Breast Ultrasound.
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  • Improved Spatial Gray Level Dependence Matrices for Texture Analysis

Abstract Views: 208  |  PDF Views: 211

Authors

Olfa Ben Sassi
Advanced Technology for Medicine and Signals ATMS, ENIS, University of Sfax, Tunisia
Lamia sellami
Advanced Technology for Medicine and Signals ATMS, ENIS, University of Sfax, Tunisia
Mohamed Ben Slima
Advanced Technology for Medicine and Signals ATMS, ENIS, University of Sfax, Tunisia
Khalil Chtourou
Advanced Technology for Medicine and Signals ATMS, ENIS, University of Sfax, Tunisia
Ahmed Ben Hamida
Advanced Technology for Medicine and Signals ATMS, ENIS, University of Sfax, Tunisia

Abstract


In this paper, we will focus on the Spatial Gray Level Dependence Matrices SGLDM to extract the Haralick's texture features of the ultrasound breast lesions. This method relies on the manual selection of the region of interest, which results in the dependence of parameters values on the extracted region. For that reason, an improved Spatial Gray Level Dependence Matrices based on the segmented masses using active contour was applied. This method outperforms the existing SGLDM method because it allows establishing a well determined threshold for the classification of lesions.

Keywords


Texture Analysis, Co-Occurrence Matrix, Spatial Gray Level Dependence Matrices, Breast Ultrasound.