For the purpose of accuracy in detection and diagnosis, Computer-Aided Diagnosis (CAD) is preferred by radiologists for the analysis of Breast Cancer. However, the presence of noise, artifacts, and poor contrast in breast images during acquisition highlights the need for sophisticated enhancement techniques for the proper visualization of region-of-interest (ROI). In this work, contrast elevation of breast mammographic and tomographic images is performed with an improved S-Curve transform using the Particle Swarm Optimization (PSO) algorithm. The enhanced images are assessed using dedicated quality metrics such as the Enhancement Measure (EME) and Absolute Mean Brightness Error (AMBE) measurement. Although the enhancement techniques help in attaining better images, certain features relevant for diagnosis purposes are removed during the enhancement process, creating contradictions for radiological interpretation. Hence, to ensure the retention of diagnostic features from original breast tomograms and mammograms, a Discrete Wavelet Transform (DWT)-based fusion approach is incorporated, which fuses the original and contrast-enhanced images (with optimized s-curve transformation function) using the maximum fusion rule. The fusion performance is thereafter measured using the Image Quality Index (IQI), Standard Deviation (SD), and Entropy (E) as fusion metrics.
Keywords: Absolute Mean Brightness Error (AMBE); DWT; PSO and S-curve transform; breast tomographic images; contrast enhancement; fusion metrics.