Breast mass detection from mammography using iteration of gray-level co-occurrence matrix

Worldwide Health Organization (WHO) has reported that cancer is a major cause of death around the world. The most common cancer in female is breast cancer. Radiologists typically diagnose breast abnormalities and indicate their regions from mammography. However, they might sometimes fail to detect the abnormalities or miss to correctly indicate their regions. To assist them and address the issues, a computer-aided diagnosis (CAD) is generally adopted to confirm the diagnosis results and increase the diagnosis accuracy. This study focused on precise detection of mass boundary from mammography. We adapted and applied a gray-level co-occurrence matrix (GLCM) with statistical features and edge detection which were originally used for color edges extraction. We also improved the method using pre-processing and GLCM iterations with six features: mean, diagonal moment, contrast, energy, inverse difference moment, and variance to distinguish breast mass region from other breast area (background), remove breast tissue, and detect masses. Our method was evaluated with a mini-MIAS database of mammograms (MIAS). The results indicated that the improved method was more suitable for detection of well-defined, circumscribed, ill-defined and other mass types. However, our method needed to improve to detect masses that infiltrated into high dense breast area with unclear boundary such as spiculated masses. This case would be taken into account as our future works.