Optimization of Local Contrast Factor with Adaptive Brightness Improvement: Impact on Mammogram Image Analysis

Abstract

Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced low contrast images, contain artefacts and noise, as well as non-uniform illumination. These limitations can be overcame in the pre-processing stage with the image enhancement process. Therefore, in this research we developed an optimized enhancement framework where the local contrast factor is manipulated to preserve details of the image. This method aims to improve the overall image visibility without altering histogram of the original image, which will affect the segmentation and classification processes. We performed dark background removal in the image histogram at early stage to increase the efficiency of new mean histogram calculation. Then, the histogram is separated into two partitions to allow histogram clipping process to be conducted individually for underexposed and overexposed areas. Consequently, the local contrast factor optimization is conducted to preserve the image details. The results from our proposed method are compared with other methods by the measurement of peak signal-to-noise ratio, structural similarity index, average contrast, and average entropy difference. The results portrayed that our proposed method yield better quality over the others with highest peak signal-to-noise ratio of 32.676. In addition, in terms of qualitative analysis, our proposed method depicted betterlesion segmentation with smoother shape of the lesion.