Digital Mammogram Image Feature Extraction Using Local Binary Patterns

Document Type : Research Paper

Author

Department of Computer Science, Faculty of Information Technology, Zarqa University, Jordan.

10.22059/jitm.2026.108008

Abstract

Nowadays, breast cancer is a very common disease among women and causes many deaths world-wide. One of the most popular techniques to diagnose breast cancer is using computer-aided diagno-sis (CAD) techniques. It helps diagnose cancer in its early stages. It can show cancer in different forms, such as masses, density, or calcifications. It is important to note that breast density is not the same as density in normal cases. In mammograms, breast density is evaluated by the percentage of fatty tissue in the breast, which differs from glandular tissue. Moreover, calcifications and masses are also important for determining breast cancer, but breast density is mainly used to identify breast cancer risk. Feature extraction is very helpful and facilitates the classification process. An algorithm for mammogram feature extraction based on fuzzy clustering and histograms was proposed to en-hance the feature extraction process. The proposed algorithm was implemented using MATLAB. A set of mammogram images was selected from the MIAS database, and the features extracted for these selected images were successful. However, the same set of images was also processed using the K-means clustering algorithm to compare the obtained features.

Keywords

References

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Journal of Information Technology Management
Volume 18, Issue 3
2026
Pages 147-154

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