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Using CLAHE Method in the Classification of Fatty Mammograms with Abnormalities

L. Agud-Albesa, M. Boix-García, D. López-Rodriguez

Abstract



To classify fatty mammograms using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, integrating additional techniques to improve classification accuracy. Methods: The proposed approach combines wavelet-based denoising, morphological operations, and second-order statistical moment analysis. Experimental validation was carried out using the Mammographic Image Analysis Society Digital Mammography Database (MIAS-DMD), which includes 106 mammographic images (66 normal and 40 with pathological findings). Results: The proposed method achieved a sensitivity of 100% and a specificity of 96.97% in the classification of fatty mammograms. Conclusion: The results demonstrate the effectiveness of the method in accurately classifying fatty mammograms. Achieving 100% sensitivity means that all abnormal cases were correctly identified, while a specificity of 96.97% indicates a very low false-positive rate. This balance suggests that the method is both reliable and robust, making it a valuable tool to support clinical decision-making in breast cancer screening.


Keywords


Mammography, Contrast Limited Adaptive Histogram Equalization, Wavelet Analysis, Morphological Operations.

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