AIRCC's International Journal of Computer Science and Information Technology

Olfa Ben Sassi ¹, Lamia sellami ¹, Mohamed Ben Slima ¹, Khalil Chtourou ¹, Ahmed Ben Hamida ¹

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

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.

Full Text