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

Retinitis Pigmentosa is an eye disease that presents with aslow loss of vision and then evolves until blindness results. Theautomatic detection of the early signs of retinitis pigmentosa acts as agreat support to ophthalmologists in the diagnosis and monitoring of thedisease in order to slow down the degenerative process.A large body of literature is devoted to the analysis of RetinitisPigmentosa. However, all the existing approaches work on OpticalCoherence Tomography (OCT) data, while hardly any attempts have been madeworking on fundus images. Fundus image analysis is a suitable tool indaily practice for an early detection of retinal diseases and themonitoring of their progression. Moreover, the fundus camera represents alow-cost and easy-access diagnostic system, which can be employed inresource-limited regions and countries.The fundus images of a patient suffering from retinitis pigmentosa arecharacterized by an attenuation of the vessels, a waxy disc pallor andthe presence of pigment deposits. Considering that several methods havebeen proposed for the analysis of retinal vessels and the optic disk,this work focuses on the automatic segmentation of the pigment depositsin the fundus images. The image distortions are attenuated by applying alocal {\color{blue}pre-processing}. Next, a watershed transformation iscarried out to produce homogeneous regions. Working on regions ratherthan on pixels makes the method very robust to the high variability ofpigment deposits in terms of color and shape, so allowing the detectioneven of small pigment deposits. The regions undergo a feature extractionprocedure, so that a region classification process is performed by meansof an outlier detection analysis and a rule set. The experiments havebeen performed on a dataset of images of patients suffering fromretinitis pigmentosa. Although the images present a high variability interms of color and illumination, the method provides a good performancein terms of sensitivity, specificity, accuracy and the F-measure, whosevalues are 74.43, 98.44, 97.90, 59.04, respectively.