Tagged: age-related macular degeneration

Automatic Segmentation of Drusen in Fundus Image Using Non–Negative Matrix Factorization

Purpose: : The segmentation and quantitation of drusen is important for diagnosis and monitoring of retinal disease, particularly age–related macular degeneration (AMD). Many approaches to drusen segmentation have been based on heuristics, such as morphological analysis, image thresholding, etc. and require significant parameter tuning. Here we describe an unsupervised method for segmenting drusen in fundus images. Methods: : Color fundus images were acquired using the Topcon50EX fundus camera and digitized on the Nikon 2000 Coolscan. Two sets of images were considered a) leveled (using a published quadratic and spline model of green (G) channel background (R. T. Smith, Arch. Ophthalmol., 2005; 123:200–6.)) and b) unleveled. A “gold–standard” was constructed by manual segmentation of the drusen by a retinal specialist. Data from the three channels (R,G,B) were processed using non–negative matrix factorization (NMF, D. Lee, NIPS, 2001; 13:556–62). NMF decomposes a multi–variant data set (X) into two matrices: a matrix of spectral signatures (S) and their corresponding spatial distribution (A). The spatial distribution matrix (A) was analyzed using K–means clustering to label all pixels in the image into one of three classes. The entire method is unsupervised and does not require manual intervention. This method has been previously demonstrated for use in NMR based metabolomics (S. Du, Proc. EMBS, Sept., 2005). Results: : Visual inspection of the labellings produced by the algorithm tended to correspond to drusen, blood vessels and normal retinal tissue. Comparison of segmentation with the manually defined gold standard showed a range of sensitivity for detection of drusen (leveled data 51%–88%, unleveled 70–75%), with a specificity of (leveled data 78–97%, unleveled 71–97%) across four cases. Interestingly, in many cases false negatives produced by the algorithm were along the borders of the gold–standard defined drusen, indicating that individual drusen were detected by the algorithm, though their size was underestimated. Conclusions: : The NMF method is able to recover spectral signatures of drusen, as well as other anatomical structures in the retina, using only the three bands in color fundus images. Related work of our group is exploring the use of hyperspectral imaging which provides richer spectral signatures and is in fact even better suited for an NMF–based decomposition/segmentation.

Accessing the Cortical Response to Macular Disease via a Large-Scale Spiking Neuron Model of V1

Purpose:: Current efforts for assessing macular disease have focused on the retina, for instance quantitation of drusen distributions. Retinal imaging, however, does not provide a complete picture of the nature of the expected vision loss. Important to consider is how the visual cortex responds to the resulting scotomata and distortion of the retinal input. Methods:: In this study we used an anatomically and physiologically detailed spiking neuron model of V1 (Wielaard and Sajda, Cerebral Cortex. 2006 16(11) 1531-1545) to investigate the effect of macular disease on cortical activity, tuning, and selectivity. We segmented fundus images and use them as “masks” for input to our cortical simulations. The model was probed using simulated drifting sinusoidal grating stimuli. All simulations were done using monocular input. We analyzed the firing rates and orientation selectivity of cells in parvocellular (4Cß) and magnocellular (4Cα) versions of the cortical model as a function of normal and abnormal retinal input. To analyze orientation selectivity we computed the circular variance (CV) across the population of cells. Results:: We found for the magnocellular model an overall reduction of firing rates of all cortical neurons. However there were no obvious “holes” of activity indicative of clusters of inactive neurons whose spatial position could be correlated with the spatial distribution of drusen. Analysis of orientation selectivity showed a dramatic reduction in selectivity for the normal vs abnormal cases. For the abnormal cases there was a shift of the CV distribution toward 1.0, indicating poorer orientation selectivity of the cells in 4Cα. For 4Cß the results are somewhat different. Unlike the magnocellular model, the parvocellular model showed clusters of inactivity which correlated with the spatial distribution of drusen. However the orientation selectivity was not significantly affected, with distributions between normal and abnormal cases being indistinguishable. Conclusions:: The magno system appears to fill-in spatial information though at the cost of a loss of orientation selectivity, were as the parvo system maintains orientation selectivity however with scotoma present in the cortical activity. This analysis is only “first order” in that drusen are treated purely as masking out the visual input, when in fact their effect on retinal ganglion cell activity can be more complex. Nonetheless, the simulations offer some insight into how responses of cortical neurons are affected by retinal disease.

Perceptual Consequences of Macular Disease Evaluated Using a Model of V1

Purpose: : Clinical assessment of macular disease typically relies on direct analysis of retinal imaging, which does not necessarily provide a complete picture of expected vision loss. A potential advancement is a framework for predicting how retinal disease affects cortical activity and ultimately perceptual performance. Methods: : Fundus images for low-vision patients with macular disease were segmented to create masks, used to simulate disease-specific distortion at the level of the retina. A 2-AFC perceptual task was designed with the goal to discriminate face and car images in the presence of noise. 10 subjects with normal vision performed the task and their results were assessed via psychometric curves. We simulated the cortical activity given the stimuli and used linear decoding of spike trains to generate neurometric curves for the model. The sparse linear decoder was optimized to maximize discrimination and not to match subjects’ psychometric curves. We simulated the cortical activity of low-vision subjects using the mask-distorted stimuli and carried out the decoding analysis in the same manner as normal subjects. Results: : Shown are the mean psychometric curve for normal subjects (red), individual subjects (light red), mean neurometric curve for simulated “normal” subjects (black), and a simulated “low-vision” subject (gray). The mean simulated “normal” subject has a neurometric curve that is a reasonable match to normal subjects, for the most part falling within the inter-subject variation. For the simulated “low vision” case, the neurometric curve is shifted to the right indicating degradation in perceptual performance. Conclusions: : Our results are promising in that they predict healthy subject perceptual performance and also result in systematic shifts in performance for simulated “low-vision” cases. Future work will quantify the predictive value of the model for a population of low-vision patients.

Coupling Retinal Imaging With Psychophysics to Assess Perceptual Consequences of AMD

Purpose: Retinal imaging does not necessarily provide a complete picture of expected vision loss for macular disease. We use a psychophysics test coupled with computational modeling to relate pathologies, found via fundus imaging, to expected perceptual function for a group of AMD patients. Methods: We recruited 10 low-vision patients with mild yet progressive AMD, as well as 10 age-matched healthy controls at the Edward Harkness Eye Institute, Columbia Presbyterian Medical Center. Both patients and controls, whose ages ranged from 65 to 84, were corrected to 20/20 to 20/50 visual acuity. All the subjects participated in a 2-AFC perceptual task, in monocular mode, where they were required to discriminate face and car images in the presence of variable noise. Color fundus photographs were collected using a Zeiss FF 450 Plus camera. Fundus images were segmented using a robust and automated algorithm to quantify disease-specific pathologies on the retina. We mapped each patient’s retinal pathology to cortical activity and neurometric curves using a computational model of V1 and a decoding framework. We compared the psychometric curves between controls and patients, and investigated the quality of the neurometric predictions. We further analyzed the correlation between the neurometric curves with statistics of drusen in the masks. Results: AMD patients had substantially lower discrimination accuracies compared to controls. Moreover, the degradation in the discrimination accuracy of AMD patients was much more pronounced at higher signal-to-noise (SNR) levels of the stimulus. We observed a positive correlation (r = 0.67) between the fraction of drusen free area on the mask with the predicted perceptual discrimination at the highest SNR level for the stimulus. Conclusions: The psychophysics and modeling framework we developed provides a quantitative assessment for the perceptual consequences of AMD and can potentially serve as a method for relating clinical findings in retinal imaging to perceptual function.