Comparison of the effectiveness of three retinal camera technologies for malarial retinopathy detection in Malawi.

The purpose of this study was to test the suitability of three available camera technologies (desktop, portable, and i-phone based) for imaging comatose children who presented with clinical symptoms of malaria. Ultimately, the results of the project would form the basis for a design of a future camera to screen for malaria retinopathy (MR) in a resource challenged environment. The desktop, portable, and i-phone based cameras were represented by the Topcon, Pictor Plus, and Peek cameras, respectively. These cameras were tested on N=23 children presenting with symptoms of cerebral malaria (CM) at a malaria clinic, Queen Elizabeth Teaching Hospital in Malawi, Africa. Each patient was dilated for binocular indirect ophthalmoscopy (BIO) exam by an ophthalmologist followed by imaging with all three cameras. Each of the cases was graded according to an internationally established protocol and compared to the BIO as the clinical ground truth. The reader used three principal retinal lesions as markers for MR: hemorrhages, retinal whitening, and vessel discoloration. The study found that the mid-priced Pictor Plus hand-held camera performed considerably better than the lower price mobile phone-based camera, and slightly the higher priced table top camera. When comparing the readings of digital images against the clinical reference standard (BIO), the Pictor Plus camera had sensitivity and specificity for MR of 100% and 87%, respectively. This compares to a sensitivity and specificity of 87% and 75% for the i-phone based camera and 100% and 75% for the desktop camera. The drawback of all the cameras were their limited field of view which did not allow complete view of the periphery where vessel discoloration occurs most frequently. The consequence was that vessel discoloration was not addressed in this study. None of the cameras offered real-time image quality assessment to ensure high quality images to afford the best possible opportunity for reading by a remotely located specialist.

Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.

Automated detection of malarial retinopathy-associated retinal hemorrhages.

PURPOSE: To develop an automated method for the detection of retinal hemorrhages on color fundus images to characterize malarial retinopathy, which may help in the assessment of patients with cerebral malaria. METHODS: A fundus image dataset from 14 patients (200 fundus images, with an average of 14 images per patient) previously diagnosed with malarial retinopathy was examined. We developed a pattern recognition-based algorithm, which extracted features from image watershed regions called splats (tobogganing). A reference standard was obtained by manual segmentation of hemorrhages, which assigned a label to each splat. The splat features with the associated splat label were used to train a linear k-nearest neighbor classifier that learnt the color properties of hemorrhages and identified the splats belonging to hemorrhages in a test dataset. In a crossover design experiment, data from 12 patients were used for training and data from two patients were used for testing, with 14 different permutations; and the derived sensitivity and specificity values were averaged. RESULTS: The experiment resulted in hemorrhage detection sensitivities in terms of splats as 80.83%, and in terms of lesions as 84.84%. The splat-based specificity was 96.67%, whereas for the lesion-based analysis, an average of three false positives was obtained per image. The area under the receiver operating characteristic curve was reported as 0.9148 for splat-based, and as 0.9030 for lesion-based analysis. CONCLUSIONS: The method provides an automated means of detecting retinal hemorrhages associated with malarial retinopathy. The results matched well with the reference standard. With further development, this technique may provide automated assistance for screening and quantification of malarial retinopathy.