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Detection of Masses in Digital Mammogram Using Second Order Statistics and Artificial Neural Network


Affiliations
1 Universiti Teknologi Malaysia, Malaysia
2 UAE University, United Arab Emirates
 

When researchers are studying the detection of breast tumors, there has been great attention to the modern textural features analysis of breast tissues on mammograms. Detecting of masses in digital mammogram based on second order statistics has not been investigated in depth. During this study, the breast cancer detection was based on second order statistics. The extraction of the textural features of the segmented region of interest (ROI) is done by using gray level co-occurrence matrices (GLCM) which is extracted from four spatial orientations; horizontal, left diagonal, vertical and right diagonal corresponding to (0°, 45°, 90° and 135°) and two pixel distance for three different block size windows (8x8, 16x16 and 32x32) . The results show that the GLCM at 0°, 45°, 90° and 135° with a window size of 8X8 produces informative features to classify between masses and non-masses. Our method was able to achieve an accuracy of 91.67% sensitivity and 84.17% specificity which is comparable to what has been reported using the state-of-the-art Computer-Aided Detection system.

Keywords

Artificial Neural Network, Mammogram, Masses, GLCM.
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  • Detection of Masses in Digital Mammogram Using Second Order Statistics and Artificial Neural Network

Abstract Views: 129  |  PDF Views: 50

Authors

Al Mutaz M. Abdalla
Universiti Teknologi Malaysia, Malaysia
Safaai Dress
Universiti Teknologi Malaysia, Malaysia
Nazar Zaki
UAE University, United Arab Emirates

Abstract


When researchers are studying the detection of breast tumors, there has been great attention to the modern textural features analysis of breast tissues on mammograms. Detecting of masses in digital mammogram based on second order statistics has not been investigated in depth. During this study, the breast cancer detection was based on second order statistics. The extraction of the textural features of the segmented region of interest (ROI) is done by using gray level co-occurrence matrices (GLCM) which is extracted from four spatial orientations; horizontal, left diagonal, vertical and right diagonal corresponding to (0°, 45°, 90° and 135°) and two pixel distance for three different block size windows (8x8, 16x16 and 32x32) . The results show that the GLCM at 0°, 45°, 90° and 135° with a window size of 8X8 produces informative features to classify between masses and non-masses. Our method was able to achieve an accuracy of 91.67% sensitivity and 84.17% specificity which is comparable to what has been reported using the state-of-the-art Computer-Aided Detection system.

Keywords


Artificial Neural Network, Mammogram, Masses, GLCM.