Tagged: imaging/image analysis: clinical

Hyperspectral Signatures of Rabbit Retina Sections

Purpose: : These studies were carried out to determine the spectral signatures of retinal structures which can be used for analysis and automated diagnosis of retinal disease. Since conventional pathology cannot determine the nature of retinal lesions in–situ, non–invasive methods must be used to quantify retinal pathology. One such a method may be by applying multispectral and hyperspectral methods and automated data analysis. Methods: : Unstained cross–sections of rabbit retinas mounted on glass slides were placed under a microscope and illuminated by white light. A monochromatic CCD camera combined with a liquid crystal tunable filter operating in the visible range was used to record the images at 10 nm intervals between 440 nm (blue) and 720 nm (red). Two methods were used to characterize the spectral signatures of the constituent tissues. The first required manual segmentation and consisted of determining the gray scale values as a function of frequency of the reflected light for the neural retina, the RPE, the choroid, and the sclera. The second used an unsupervised decomposition called non–negative matrix factorization (NMF) for the same four layers. NMF decomposes a multivariate data set into two matrices; a matrix of spectral signatures and their corresponding spatial distribution. Results: : The reflectance spectrum of each of the tissue layers obtained by the manual method formed a characteristic curve (signature) distinct in the frequency range studied and different for each layer. The signatures recovered using NMF have spatial distributions consistent with those obtained with manual segmentation. Both were consistent in recovering four distinct signatures. Conclusions: : Spectral signature characteristic of each of retinal layers investigated appears to be unique by both methods. As such these signatures lend themselves to being a tool for diagnosing retinal lesions that may have a different neural retina, RPE, choroidal, or scleral component.

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.