Retinal model-based visual perception: Applied for medical image processing

The Human Visual System (HVS) model based image quality metrics, correlates strongly with the evaluations of image quality as well as with human observer performance in the visual recognition process. Physiological modeling of retina plays a vital role in the development of high-performance image processing methods for better visual perception. For image processing in medical diagnosis, one has to follow several steps like image preprocessing, image segmentation, feature extraction, image recognition, and interpretation. This work aims at developing human visual system based image processing which stands advantageous when compared with the conventional processing methods. The main aim of this work is to develop a model for retina, which has complex neural structure, capable of detecting the incoming light signal and transforms the signal before transmitting it through the optic nerve. This retinal model comprises of the photoreceptor, outer-plexiform and inner-plexiform layers exhibiting the properties of compression and spatiotemporal filtering in the processing of visual information. The spatial frequency value is evaluated using Discrete Cosine Transform (DCT) technique thereby enhancing the contrast visibility in the dark area and maintaining the same in the bright area using photoreceptor layer of the retina. Contour contrast enhancement is achieved by modeling outer- plexiform layer of retina and parvo channel of the inner-plexiform layer is modeled to extract finer details of the image. The properties like luminance, spatial and temporal frequencies were considered to develop the human visual system based retinal model. The proposed model is applied to a wide variety of medical images and with simulated results it has been proved that the texture feature values of the processed image are found to be higher than the original input image. Further, this method proves to be more flexible which enables easier practical implementation when compared to that of generic medical image processing techniques.