A computer-aided algorithm was trained to detect different types of pathologic retinal conditions. The cases of hard exudates within 1 disc diameter (DD) of the fovea (surrogate for CSME) were detected with very high accuracy (sensitivity = 1, specificity = 0.50), whereas mild nonproliferative DR was the most challenging condition (sensitivity = 0.92, specificity = 0.50). The algorithm was also tested on images with signs of AMD, achieving a performance of AUC of 0.84 (sensitivity = 0.94, specificity = 0.50).
These results show that this algorithm can be used to automatically grade papilledema. The algorithm provides objective and quantitative assessment of the stage of papilledema with accuracy that is comparable to grading by a neuro-ophthalmologist. One application is in rapid assessment of digital optic nerve photographs acquired in clinical, intensive care, and emergency response settings by nonophthalmologists to evaluate for the presence and severity of papilledema, due to intracranial hypertension.
Retinal vasculature is one of the most important anatomical structures in digital retinal photographs. Accurate segmentation of retinal blood vessels is an essential task in automated analysis of retinopathy. This paper presents a new and effective vessel segmentation algorithm that features computational simplicity and fast implementation. This method uses morphological pre-processing to decrease the disturbance of bright structures and lesions before vessel extraction. Next, a vessel probability map is generated by computing the eigenvalues of the second derivatives of Gaussian filtered image at multiple scales. Then, the second order local entropy thresholding is applied to segment the vessel map. Lastly, a rule-based decision step, which measures the geometric shape difference between vessels and lesions is applied to reduce false positives. The algorithm is evaluated on the low-resolution DRIVE and STARE databases and the publicly available high-resolution image database from Friedrich-Alexander University Erlangen-Nuremberg (Germany). The proposed method achieved comparable performance to state of the art unsupervised vessel segmentation methods with a competitive faster speed on the DRIVE and STARE databases. For the high resolution fundus image database, the proposed algorithm outperforms an existing approach both on performance and speed. The efficiency and robustness make the blood vessel segmentation method described here suitable for broad application in automated analysis of retinal images.
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