AN EMPIRICAL APPROACH TO MAMMOGRAM IMAGE ENHANCEMENT BASED ON PRIMARY IMAGE ANALYSIS

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Early detection of breast cancer significantly improves treatment outcomes, and mammography remains one of the most widely adopted screening techniques. However, the diagnostic accuracy of mammogram images is often affected by low contrast, poor illumination, and the presence of noise, which may obscure clinically important features. Image enhancement therefore plays a crucial role in improving the visual quality of mammogram images and assisting radiologists in accurate interpretation. This paper presents an empirical approach for mammogram image enhancement based on primary image analysis techniques. The proposed methodology focuses on classical image processing operations such as contrast stretching, histogram-based enhancement, and local statistical analysis to improve image visibility while preserving essential structural information. Unlike learning-based approaches, the method does not require training data and operates directly on pixel intensity distributions. Experimental analysis is carried out on a set of representative mammogram images, and the effectiveness of the enhancement is evaluated using histogram analysis and visual assessment. Comparative results demonstrate that the enhanced images exhibit improved contrast distribution, better visibility of tissue regions, and reduced intensity overlap when compared to the original images. The simplicity, computational efficiency, and independence from complex modeling make the proposed approach suitable for early-stage computer-aided diagnosis systems and clinical environments with limited computational resources.

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Keywords

Mammogram image enhancement, Medical image processing, Histogram analysis, Contrast enhancement, Primary image analysis, Digital image processing, Breast cancer screening, Image quality improvement, Computer-aided diagnosis.