Automatic segmentation of pigment deposits in retinal fundus images of Retinitis Pigmentosa disease

Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography (OCT) data, while hardly any attempts have been made working on fundus images. Fundus image analysis is a suitable tool in daily practice for an early detection of retinal diseases and the monitoring of their progression. Moreover, the fundus camera represents a low-cost and easy-access diagnostic system, which can be employed in resource-limited regions and countries. The fundus images of a patient suffering from retinitis pigmentosa are characterized by an attenuation of the vessels, a waxy disc pallor and the presence of pigment deposits. Considering that several methods have been proposed for the analysis of retinal vessels and the optic disk, this work focuses on the automatic segmentation of the pigment deposits in the fundus images. The image distortions are attenuated by applying a local {\color{blue}pre-processing}. Next, a watershed transformation is carried out to produce homogeneous regions. Working on regions rather than on pixels makes the method very robust to the high variability of pigment deposits in terms of color and shape, so allowing the detection even of small pigment deposits. The regions undergo a feature extraction procedure, so that a region classification process is performed by means of an outlier detection analysis and a rule set. The experiments have been performed on a dataset of images of patients suffering from retinitis pigmentosa. Although the images present a high variability in terms of color and illumination, the method provides a good performance in terms of sensitivity, specificity, accuracy and the F-measure, whose values are 74.43, 98.44, 97.90, 59.04, respectively.