A New Correction Factor-based Strategy with DWT-SVD for Contrast Enhancement in Digital Mammograms
Keywords:
Mammograms, Contrast Enhancement, Discrete Wavelet Transform, Adaptive Gamma Correction, Singular Value DecompositionAbstract
Globally, breast cancer stands as the second most prevalent disease affecting women. Mammography, utilizing low-dose X-rays, remains a highly effective modality for the early detection of cancer. Challenges such as uneven illumination and machine-imposed limitations contribute to low-contrast mammogram images, potentially impacting the accuracy of diagnoses. Due to the inherently narrow intensity range in mammography images, distinguishing between cancerous and non-cancerous tissues becomes challenging. This paper introduces a novel approach that combines Adaptive Gamma Correction with a two-way Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) to enhance the visual clarity of the resulting images while preserving crucial clinical information. The introduction of a new correction adjustment factor enhances the singular value of the image, resulting in a significantly improved contrast-enhanced output. Experimental validation is conducted using mini-MIAS dataset, assessing the proposed technique with quantitative parameters such as Structural Similarity Index Measurement (SSIM), Pearson Correlation Coefficient (PCC), Peak to Signal Noise Ratio (PSNR), Contrast Improvement Index (CII), Mean Absolute Error (MAE), and Average Mean Brightness Error (AMBE). The obtained average values, including scores of 0.929, 0.998, 22.875, 1.136, 14.457, and 14.138, respectively, demonstrate promising results compared to conventional methods. Furthermore, comparison with the state-of-the-art techniques shows improved results, showcasing significant advancements in local information preservation and contrast enhancement in mammography images.
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