Evaluating Mammograms Enhancement Techniques Using Quantitative Metrics

In medical field, image enhancement techniques could play a crucial role in terms of providing accurate diagnosing and tumour classification. For instance, mammography is an imaging technique that is widely used to detect and diagnose breast cancer. Hence, improving the quality of the mammograms helps physicians by providing clear and fine images. This research investigates the performance of three enhancement techniques to improve and enhance the mammograms. These techniques are Gamma Correction (GC), Adaptive Histogram Equalization (AHE), and Linear Contrast Enhancement (LCE), and were chosen due to their efficiency in medical image enhancement. Signal to Noise Ratio (SNR), Entropy, Structural Similarity Index (SSIM), and Average Gradient (AG) are the performance metrics that were used to evaluate the three techniques. Results showed that AHE outperformed the GC and LCE in terms of producing high SNR, Entropy and AG. However, GC and LCE showed better values for the SSIM and similar values for the SNR, Entropy, and AG. This study concludes that AHE could be preferable choice for enhancing mammograms.