Mammographic Image Enhancement Using a Linear Weighted Fusion of Contrast Improvement Technique and CLAHE

Breast cancer is a global health concern, and early diagnosis is key to successful treatment. Digital mammography is the primary imaging modality for breast cancer detection. Nevertheless, mammograms often exhibit issues such as low contrast, background noise, and incoherent contrast among regions, making accurate diagnosis challenging. To address these challenges, a novel image enhancement technique is proposed in this research. The primary goal of this study is to enhance mammography images, thereby improving the segmentation and classification processes for breast cancer diagnosis. Hence, this research introduces a distinctive technique for enhancing mammogram images. The approach involves segmenting the image into two parts using predefined thresholds, followed by independent equalization of each part. To achieve a high-quality image, a weighted fusion process is employed, integrating the Contrast Limited Histogram Equalization (CLAHE) method. The outcomes are assessed using sample mammogram images, and the performance is validated through key measures such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and entropy.