Development of mathematical morphology filter for medical image impulse noise removal

Post-acquisition denoising of medical images is of importance for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. During the image generation, imaging devices are quite often interfered by various noise sources. Impulse noise which causes the medical images to remove important image details such as edges, contours and texture. In this paper, a new filtering method is proposed to remove impulse noise on degraded medical images. The proposed filter is integrated with noise detector and filtering approach. An impulse noise detector using mathematical residues is proposed to identify pixels that are corrupted by impulse noise, and the image is recovered using specialized open-close algorithm that is only applied to the noisy pixels. Black and white blocks that degrade the quality of the image will be recovered by a block smart erase method. The proposed method was tested on simulated medical images from a brain web database and clinical medical images with different levels of noise. The results show that the morphology filter produces better denoising results in terms of qualitative and quantitative measures compared with other denoising methods, compared with several existing noise filtering models demonstrated that not only the proposed filter is effective for noise removal but also for image detail preservation and clinical practice.