Medical images contrast and detail enhancement using a multiscale morphological approach

Medical images are used to analyze and diagnose diseases. The analysis of these diseases requires images with high contrast and detail, because they contain important information about the disease. Capture methods are improving every day, but sometimes the quality of medical images is not adequate to make a good medical diagnosis. Some common problems are ambient noise, poor lighting conditions, or technical limitations of the devices generating low quality medical images. For these cases, contrast enhancement techniques, which enhance the visual quality of the images, are generally useful. However, there is still room for improvement. In this paper, we propose a novel algorithm to improve the contrast and detail of medical images. This proposal is based on a multiscale morphological approach that uses two structuring elements in the top-hat transform. The simulations were performed with medical images taken from public repositories. The experimental results show that the proposal enhances local contrast and improves the details of the medical images.