Adaptive Local Gray-Level Transformation Based on Variable S-Curve for Contrast Enhancement of Mammogram Images

Mammogram image enhancement plays an important role in the accuracy of the diagnosis. Resulting images must allow a good contrast and better appearance of the limiting edges between different areas in the image, while preserving both information and brightness. In this paper, an adaptive local gray-level s-curve transformation is proposed. The main aim is to improve as much contrast of mammogram images as possible. The principle is to find all the parameters of the local gray-level transformation for each image which will allow for a better improvement using the genetic algorithm that is among global optimization methods. The evaluation of the results found is done based on image quality assessment (IQA) metrics and visual inspection in comparison with three existing techniques based on histogram equalization.