Hyperspectral image analysis for ophthalmic applications

Transcription

1 Hyperspectral image analysis for ophthalmic applications Gilberto Zamora*, Paul W. Truitt, Sheila C. Nemeth, Balaji Raman, Peter Soliz Kestrel Corporation, 3815 Osuna N.E., Albuquerque, NM, USA ABSTRACT A continuing clinical need exists to find diagnostic tools that will detect and characterize the extent of retinal abnormalities as early as possible with non-invasive, highly sensitive techniques. The objective of this paper was to demonstrate the utility of a Hyperspectral Fundus Imager and related analytical tools to detect and characterize retinal tissues based on their spectral signatures. In particular, the paper shows that this system can measure spectral differences between normal retinal tissue and clinically significant macular edema. Future work will lead to clinical studies focused on spectrally characterizing retinal tissue, its diseases, and on the detection and tracking of the progression of retinal disease. Keywords: Hyperspectral imaging, retinal imaging, diabetic retinopathy, clinically significant macular edema, principal component analysis. 1. INTRODUCTION Diabetic retinopathy associated with diabetes mellitus is the most common cause of blindness in the working population. The near epidemic rate of increase in diabetes is significant, with 12.1 million cases reported in 2002 and projections of 17.4 million cases by 2020 [1]. Diabetic retinopathy in its earliest stages is characterized by increased retinal vascular permeability. This can lead to fluid accumulation in the retina, and when the fluid is located in the macular region. The macula is the anatomic region of the retina responsible for fine, detailed vision and its health is the principal factor in visual acuity. The natural history of diabetic retinopathy and maculopathy, and the basis for assessing treatment was examined in a major clinical trial, The Early Treatment Diabetic Retinopathy Study (ETDRS) [2]. It was recognized in this large study that maculopathy which had particular features, such as thickening of the retina at or within 500 microns of the center of the macula, should be strongly considered for laser treatment [3]. This type of maculopathy, as defined by the ETDRS, was termed clinically significant macular edema (CSME). New cases of CSME in the United States are reported to be between 50,000 and 75,000 per year [4, 5]. Currently, diabetic maculopathy is diagnosed clinically by examination with a biomicroscope with a very high dioptric lens (90 diopters) through a dilated pupil. This technique gives the examiner stereo clues for assessment of retinal thickening from fluid accumulation. However, the presentation of the edema can be subtle. Fluorescein Angiography (FA), a technique for viewing the passage of fluorescein dye through retinal vessels for vessel integrity, is an extremely valuable procedure which is used as a guide for treating CSME. FA, however, is an invasive test, which carries potential risks including death (one in 222,000 patients) and severe medical complications (one in 2,000 patients) [6]. Other diagnostic tools have been developed, which include the Confocal Scanning Ophthalmoscope (CSLO), Retinal Thickness Analyzer (RTA) and Optical Coherence Tomographer (OCT) [7]. The CSLO and RTA provide quantitative measurements, but are limited in their vertical (depth) resolution. OCT has the best depth resolution, but currently only obtains a one-dimensional image, so that mapping the horizontal extent of the edema requires extensive scanning. In this paper, an innovative use of a hyperspectral fundus imager is reported which simultaneously captures 110 spectral bands in the nm range with a spectral resolution of 4-8nm. This investigation compares the spectral characteristics of the retina that presents with CSME to an age-matched normal. The objective of this work is to determine whether spectral changes due to edema can be exploited to determine the presence of macular edema and then *gzamora@kestrelcorp.com; phone ; fax ; kestrelcorp.com

2 to use such spectra to define the margins of the affected region. The motivation for this research is to develop a clinical device based on the standard fundus camera that can be used to screen for CSME non-invasively. This paper is organized as follows: Section 2 explains the methodologies used for the present work including the description of subjects, instrumentation, imaging protocol, and analysis techniques; Section 3 shows the results of the study; Section 4 offers a discussion of the findings that includes possible extensions of this work into other retinal abnormalities and toxicity studies; Section 5 concludes the presentation of the work. 2. METHODS 2.1. Subjects Subjects were recruited at the University of New Mexico Health Sciences Center Division of Ophthalmology. An Institutional Review Board (IRB) and approved informed consent were obtained through the University of New Mexico Medical School. Subjects underwent a complete ophthalmic examination, including visual acuity, biomicroscopy exam, intraocular pressure and a dilated retinal examination. Retinal fundus photographs were obtained. A 33 year old male with a 15 year course of Type I diabetes mellitus and clinically significant macular edema in the left eye, confirmed and documented by fluorescein angiography, was selected for the study. The subject had recently undergone focal laser treatment of CSME in the right eye. An age matched male with a normal ophthalmic examination was selected for the study as a control. Both subjects were dilated with 1% mydriacyl and 2.5% phelylephrine. Data were collected with the hyperspectral imager across similar anatomic regions of the macula, along with data from other regions of the retina according to the imaging protocol described in section Instrumentation The equipment used in this work consisted of two components: a hyperspectral fundus imager (HSFI) and hyperspectral data analysis software (HSSW). The details behind the design and operation of the HSFI [8-13] have previously been shown. Therefore, here a brief overview of this instrument is offered. The HSSW is a software application specifically designed to analyze the data collected by the HSFI and will be briefly described below. The HSFI is based on a Fourier Transform Imaging Spectrometer (FTIS) (patent pending) coupled to a standard fundus imager (Figure 1). The imaging process starts with the operator aligning the HSFI to target a specific site on the retina using a computer monitor that presents a live video image of the retina generated by the scene camera. This scene image has a computer-generated fiducial line that indicates where the hyperspectral data will be collected. Once the operator has aligned the HSFI on the desired site, the xenon flash is initiated; the light is diverted through the FTIS and onto the data camera. A 2D panchromatic scene image is saved with each hyperspectral image that records the precise location of the hyperspectral data. The FTIS operates in the visible and near IR spectrum and collects 110 spectral bands in the 450nm-800nm range. One advantage of the FTIS for ophthalmic applications is that it captures the spectra for this range at 4-8nm resolution in one exposure of the xenon flash (Figure 2). This feature eliminates the need for inter-wavelength registration due to involuntary eye movements, i.e., saccades. Capturing the entire spectra in one flash also removes the need for normalization due to inter-flash intensity variation introduced by a number of factors including the changes in the imaging geometry and efficiency of the coupling of the light into the eye. Scene Camera Figure 1. Drawing of the HSFI system. Figure 2. Spectral resolution of the FTIS.

3 The HSFI measures spatially-resolved spectral data for each of 512 spatial pixels simultaneously along a one dimensional line. Each spatial pixel has a footprint of 150µm in the cross-path (along the imaging line) and 300µm perpendicular to the imaging line. For each imaged pixel, the FTIS produces an interference pattern that contains the spectral information of that particular region (Figure 3). Together, the 512 interference patterns form a complete interferogram image (Figure 4). Magnitude Pixel no. Bin no. Figure 3. Example of interference pattern, referred to as an interferogram. Bin no. Figure 4. Example of a raw interferogram image collected with HSFI. Each individual interferogram codes the spectral information corresponding to the spatial pixel imaged. A series of signal processing steps are used to decode this information [8-13]. This processing is based on an inverse Fourier transform applied to each individual interference pattern. The rest of the processing consists of normalization steps that produce spectrally and radiometrically calibrated data (Figure 5). When the spectra from all the imaged pixels are combined, a 3D surface is generated that shows the spatial-spectral relationships corresponding to the imaged region (Figure 6). Figure 7 shows the scene image with fiducial line indicating where HSFI data will be collected. Figure 5. Sample spectrum from one pixel. Figure 6. 3D surface generated by all pixels spectra. Ground truth, i.e., tissue type and lesion margins, was provided by a Certified Ophthalmic Medical Technologist (SCN) who manually classified the imaged pixels along the fiducial line by tissue type. This task was performed using a drawing tool which is part of HSSW. The classified pixels are saved in the form of an overlay image and are used for training and testing of the analysis algorithms. After the pixels from the scene image are classified, the HSSW locates the spectral signatures corresponding to each pixel. Examples of this ground truth extraction process are shown in the results section.

4 2.3. Imaging protocol In order to maximize the uniformity of data collected among subjects, an imaging protocol using the HSFI has been developed. This imaging protocol consists of images from five different regions on the retina. The labels used to identify these regions follow commonly used retinal field naming conventions (Figure 8): 1) Region 1: Field 1. Fiducial line across the center of the optic disc. 2) Region 2: Field 1.5. Fiducial line across optic disc and macula. 3) Region 3: Field 1.5. Fiducial line in the same horizontal position as region 2 but placed superior to the optic disc. 4) Region 4: Field 1.5. Fiducial line in the same horizontal position as region 2 but placed inferior to the optic disc. 5) Region 5: Field 2. Fiducial line across the center of the macula. Figure 7. Scene image corresponding to the spectra in Figure 6. Note the fiducial line indicating the line scanned by the FTIS. Besides the five different regions, the imaging protocol allows the HSFI operator to image any region of special interest such as lesions, drusen, hemorrhages, etc. Optic disc Fiducial line Macula Region 1 Field 1 Region 2 Field 1.5 Region 3 Field 1.5 Region 4 Field 1.5 Region 5 Field 2 Figure 8. The five retinal regions that are used in the imaging protocol for the right eye. The left eye follows the same protocol except that the images are mirrored to the left Analysis techniques The data generated by the HSFI are hyperspectral signatures of regions imaged on the retina. Analysis of these data involves multidimensional signal analysis. For the present study, Principal Component Analysis (PCA) was applied in analyzing the hyperspectral data. PCA is a well-known signal processing technique that has been used to analyze multidimensional data for different applications such as remote sensing, data compression, noise analysis, shape modeling, etc. Here we only offer a brief overview of PCA. A more detailed description of the technique and its applications can be found in [14, 15]. PCA is a statistical technique that takes multidimensional data and maps it into a new coordinate system with special characteristics. The first characteristic of the new system of coordinates is that its axes are orthogonal, thus decorrelating the components in the transformed dataset. The second characteristic of the new system of coordinates is that each of its axes describes the direction of variation of the original dataset. These two characteristics are used for two purposes. First, they describe the variability of the original dataset in terms of decorrelated components. Second, they reduce the dimensionality of the dataset by eliminating those components that contribute the least to the information conveyed by the dataset.

5 In our work, PCA is applied as follows. The spectral signature from the i-th pixel is represented by a signature vector xi=(ρ(λ1), ρ(λ2),, ρ(λn))t, where ρ(λy) is the normalized reflectance from that pixel at the y-th wavelength λy. Then the mean and covariance matrix of the entire dataset are calculated as 1 x= N N x i, (1) i =1 1 N X= ( xi x) ( xi x) T N 1 i =1, (2) respectively. The eigenvectors of X, Φm, determine the axes of the new system of coordinates, whose origin is at 0. The eigenvalues of X, δm, describe the amount of variability of the original dataset in the direction of their corresponding eigenvectors. The transformation ( b i = Φ T xi x ) T, (3) where matrix Φ consists of the concatenation of the eigenvectors corresponding to certain eigenvalues, chosen according to criterion such as variance, maps the signature vector xi into the new system of coordinates spanned by Φ. Vector bi, called a signature parameter vector, contains the contribution of xi to the new system of coordinates. The application of PCA in our work is as follows. Each element of the signature parameter vector bi is a quantitative measure of the variation of the signature vector xi in the direction of each eigenvector. Therefore, if each eigenvector defines a mode of variation, then each element of b determines the magnitude of such variation. Since in our work we collected spectral signatures from different tissues, it is our hypothesis that it is possible to use the signature parameter vector b to describe the distinguishing characteristics of spectral signatures, i.e., b becomes a spectral feature vector. 3. RESULTS This section describes the findings from analysis of the hyperspectral data that was collected with the HSFI using the PCA techniques described previously. Data from the two subjects described in Section 2 were collected following our imaging protocol. The first subject, referred to as T1, was the control subject, i.e., no known retinal abnormalities. Figure 9 shows the grayscale version of a RGB image taken from the second subject, T2, who presents with CSME. As described in the introduction, invasive FA is effective for detecting the presence of CSME and determining its margins. Figure 10 shows one FA image from the late stage of the FA video that shows the extent of the CSME as a bright spot due to dye leakage. CSME Figure 9. Image from subject T2 ME-affected macular tissue. Figure 10. FA image from subject T2 showing extent of CSME.

6 3.1 Spectral data Next presented is a series of spatial and spectral images that serve as a comparison of features between the two subjects in the study, T1, control, and T2, CSME. Figure 11 shows the scene and spectral images from subject T1, control, taken from region 1, optic disc. Figure 12 shows the same region from subject T2, CSME. In both images the different anatomical features imaged with the horizontal data line across the center of the optic nerve are apparent in the variation in the spectral surfaces. In both figures the major retinal vessels (darker, least reflectance area) exiting the optic nerve produce their spectral signatures at approximately 580nm, the absorption wavelength of hemoglobin. The optic nerve head tissue has the largest degree of normalized spectral reflectance in the nm range. Of interest is the comparison of the optic nerve profiles between these subjects, as T2 has a higher degree of reflectivity across the entire disc region. This may be explained by the fact that T2 has a larger physiologic cup than T1, and therefore has a greater area of reflectivity than T1. Figure 11. Data from subject T1, region 1. Scene image and spectral surface. Vessel Optic disc Vessel Figure 12. Data from subject T2, region 1. Scene image and spectral surface. Optic disc Figures 13 and 14 show the scene and spectral images taken from region 3, superior to optic disc and macula, from subjects T1, control, and T2, CSME, respectively. In T2, the retinal vein is evident in the spectral surface at approximately pixel 250 with high absorption (i.e., low reflectance) relative to the rest of the features. Of interest is the difference in the normalized reflectance between the subjects at the nm wavelength. Also in T2, the signature has a higher degree of reflectance as demonstrated by the increased magnitude of the spectra. As a preliminary speculation, this could represent sub-clinical specular differences of retinal tissue between a normal and a diabetic

7 subject. The compromise of the blood-retinal barrier in the diabetic cause increased permeability into the extracellular spaces of the retina. With an increase in fluid at the tissue level, this could cause an increase in the tissue reflectance. Figure 13. Data from subject T1, region 3. Scene image and spectral surface. Vessel Figure 14. Data from subject T2, region 3. Scene image and spectral surface. Figures 15 and 16 show the scene and spectral images taken from region 5, macula, from subjects T1, control, and T2, CSME, respectively. In these two images the low reflectance nature of the macula is apparent. In Figure 16, the diabetic subject, there is a slight increase in spectral reflectance in the nm range. The normal subject demonstrates an absorption pattern across the pixels at the 580nm wavelength whereas the diabetic subject does not demonstrate this linear pattern, but rather a higher degree of reflectance around the fovea. Increased reflectance in this area could represent increased fluid content of tissues, i.e., macula edema.

8 Macula Figure 15. Data from subject T1, region 5. Scene image and spectral surface. Macula Figure 16. Data from subject T2, region 5. Scene image and spectral surface. As described in Section 2, ground truth was extracted by selecting the pixels corresponding to the tissues of interest from the scene image by a Certified Ophthalmic Medical Technologist (SCN). For the work presented in this paper, the area of interest was the macula of the two subjects. In the control subject, T1, four different areas representing different tissue types were selected: retinal background (RB), retinal background-macula transition (RB-MA), macula (MA), and fovea (FO). In the diabetic subject, T2, three different regions were selected: MA, macula-csme transition (MA- CSME), and definite CSME (CSME). Figure 17 shows an example of the scene image and corresponding overlay image for subject T1. Figure 18 shows the scene image and corresponding overlay image for subject T2. Macula Retinal background RB-MA transition Fovea Figure 17. Macula of subject T1, control, scene image and corresponding overlay image (detail).

9 CSME-Macula Macula CSME Figure 18. Macula of subject T2, CSME, scene image and corresponding overlay image. Figure 19 shows the spectral signatures of the retinal tissue imaged from subject T1, whereas Figure 20 does the same for T2. From these plots, it can be noted that clear distinction between tissues is difficult using only the raw spectral signatures. Therefore, some analytical tools, such as PCA, are employed. (c) Figure 19. Spectral signatures from subject T1. RB, MA, (c) FO. (c) Figure 20. Spectral signatures from subject T2. MA, MA-CSME, (c) CSME. 3.2 Tissue characterization PCA was applied to the spectral signatures collected from our two subjects. Two experiments were performed. The first experiment consisted of applying PCA to the whole dataset of spectral signatures and analyzing the resulting eigenvectors. The second experiment consisted of applying PCA separately to the spectral signatures of subjects T1 and T2 and analyzing the resulting eigenvectors. In the first experiment, when the spectral signatures from both subjects were processed, PCA extracted 11 eigenvectors describing 99.5% of the variability of the dataset. The first eigenvector, i.e., the one describing the largest amount of

10 variability on the dataset, 80.31%, described the overall offset of the spectral signatures, i.e., overall reflectivity from the retina. The second, 7.69% of dataset s variability, described reflectance variability around four anchor wavelengths, i.e., wavelengths at which the reflectance presented no variation: 506.9nm, 510.5nm, 512.5nm, and 596.2nm. The third eigenvector, 3.16% of dataset s variability, described reflectance variability around 10 anchor wavelengths: 526nm, nm, nm, nm, nm, nm, nm, nm, nm, and nm. Eigenvectors 4 through 11 described less of dataset s variability and found even more anchor wavelengths. In the second experiement, when only the spectral signatures from T1 were processed, PCA found 7 eigenvectors describing 99.5% of the dataset s variability. As in the previous case, the first eigenvector represented the overall reflectance of the retina and described 81.58% of dataset s variability. The second eigenvector, 8.94% of dataset s variability, had only two anchor wavelengths: nm and nm. The third eigenvector, 4.16% of dataset s variability, had 11 anchor wavelengths. When only spectral signatures from T2 where processed, PCA found 5 eigenvectors describing 99.5% of the dataset s variability. In contrast with the previous two cases, here the first eigenvector, 68.2% of dataset s variability, had two anchor wavelengths: 510.6nm and 511.3nm. The second eigenvector, 14.86% of dataset s variability, had also two anchor wavelengths, 514.5nm and nm. The third eigenvector, 8.6% of dataset s variability, had 13 anchor wavelengths. When individual elements of the signature parameter vector b, the spectral feature vector, were inspected in the three cases described above, not one was determined to serve as the sole descriptor of the spectral signatures. Instead, when combinations of parameters were inspected, it was found that the first three parameters, corresponding to the first three eigenvectors, could be used to cluster the spectral signatures. However, this clustering did not show perfect separation between the different types of tissue when using the whole dataset of spectral signatures (Figure 21). Figure 21. Clustering of spectral parameters for all tissue types. ( ): T1, RB; (x): T1, MA; (*): T1, FO; (o): T2, MA; ( ): T2, MA-CSME; ( ): T2, CSME. In contrast, when using the spectral signatures from the two subjects separately, it was found that the first three spectral parameters could describe the different tissues in well-formed clusters. Furthermore, it was found that the cluster separation was larger for subject T2, who presented with CSME. This result suggests that the tissue variability as captured by the HSFI is more pronounced in the regions affected by CSME, i.e., CSME affects the spectral characteristics of the underlying tissue (Figure 22), and this appears to support the proposed hypothesis.

11 Figure 22. Clustering of tissue parameters from T1. Clustering of tissue parameters from T2. ( ): T1, RB; (x): T1, MA; (*): T1, FO; (o): T2, MA; ( ): T2, MA-CSME; ( ): T2, CSME. 4. DISCUSSION In the previous section, it was shown how PCA can be applied to analyze hyperspectral data from the retina of two subjects, one control (T1) and one affected by CSME (T2). In the experiments described, PCA extracted several eigenvectors which were then analyzed. The spectral signatures were also reconstructed using each individual eigenvector. This back-mapping of the spectral signatures gave insight into the meaning of each eigenvector in the spectral domain. In two of the experiment, T1-plus-T2 data and T1 data only, the first eigenvector described the overall offset of the spectra, i.e., a change of overall reflectance. In this regard, two observations can be made. First, the overall reflectance was captured by the first eigenvector as it represented the highest variability in the data set. This result is due in part to a non-uniform coupling of the instrument to the subject s eye due to a minute misalignment of the instrument at the moment the data were collected. This misalignment results in putting more light onto some regions which then appear brighter. This effect can be minimized by ensuring optical alignment of the instrument prior to acquisition. The second and predominant reason is that overall reflectivity varies spatially across the retina. In the processing methodology, the contribution of melanin in the longer wavelengths, i.e., longer than 600nm, was taken into account. However, several other ocular pigments play a significant role in the overall reflectance [16] and must be considered. The second observation regarding the extracted eigenvectors is that for the data from subject T2, no eigenvector described overall reflectance. This means that other sources of spectral variability had a more prominent contribution that overall reflectance due to coupling and/or gross ocular pigmentation. In the case of the diabetic subject, T2, this spectral variability may be associated with changes in cellular structure. In regard to the remaining eigenvectors, it is interesting to note that they describe reflectance variability around anchor wavelengths, which were defined as wavelengths of null reflectance variability. These anchor wavelengths may define boundaries for ranges of reflectance variability which might be used to characterize both normal and abnormal tissue. A closer examination of this effect is the focus of a future study. The results show another difference between the two subjects. Specifically, the features from the various tissues cluster differently. The data from T1 produced clusters in a very close proximity to each other, making it difficult to differentiate them (see Figure 22a), while the data from T2 results in clusters with evident separation (See Figure 22b). This clustering difference suggests that CSME has a detectable effect on the cellular structure of the underlying tissue. An alternative approach to that presented here is to consider the sampled data as coming from a mixture of two different tissues, normal macula and edema. Since PCA is not well posed for signal unmixing, another technique should be used such as Blind Source Separation (BSS). BSS analyzes a measured signal and decomposes it into a pre-defined number of source signals assuming either a linear or a non-linear mixing process. In principle, this type of analysis may provide insight into the changes undergone by non abnormal tissue. This approach will be investigated in a future study.

12 5. CONCLUSIONS This work represents a step forward an effort to show the utility of hyperspectral imaging for ophthalmic applications. The purpose of this work is to translate the success of this technique from other areas such as remote sensing to the detection, characterization, and treatment of retinal diseases. It has been previously shown that the hyperspectral fundus imager has the capability to collect hyperspectral data from the retina. The present work was focused on demonstrating the value of this type of data in extracting information that allows the distinction of different retinal tissues including those affected by disease. In particular, it has been shown that the technique of Principal Component Analysis can be used to extract useful features from spectral signatures. This technique showed that there are spectral differences between different retinal tissues and even more important, that there are spectral differences between normal and disease-affected tissues of the same type. These results are the basis to design complete clinical studies targeted at showing that hyperspectral retinal imaging and its related analytical tools can be used to improve the quality of assessment of retinal status by reducing variability and increasing accuracy with respect to expert human assessment. ACKNOWLEDGMENTS This work was sponsored by National Medical Technology Testbed, Inc. (NMTB) and the department of the Army, Cooperative Agreement Number (DAMD ). The content of the information in this work does not necessarily reflect the position or the policy of the government or NMTB. No official endorsement should be inferred. REFERENCES 1. American Diabetes Association, Economic Costs of Diabetes in the U.S. in Diabetes Care 2003; 26: D.S. Fong, F.L. Ferris, M.D. Davis, and E.Y. Chew, ETDRS Research Group: Causes of severe visual loss in the Early Treatment Diabetic Retinopathy Study. ETDRS Report No. 24. Am J Ophthalmol 127: , Early Treatment Diabetic Retinopathy Study Research Group: Focal photocoagulation treatment of diabetic macular edema. ETDRS Report Number 19. Arch Ophthalmol 113: , L.P. Aiello, J.D. Cavallerano, T.W. Gardner, F.L. Ferris, G.L. King, R. Klein, and G. Blankenship, Diabetic retinopathy. Diabetes Care, 1998; 21: E. Steffinsson, T. Bek, M. Porta, N. Larsen, J.K. Kristinsson, and E. Agardh, Screening and prevention of diabetic blindness. Acta Ophthalmol Scand 2000; 78: L.A. Yannuzzi, et al, Fluorescein angiography complication survey, Ophthalmology, 1986, 93: C. Strom, B. Sander, N. Larsen, M. Larsen, and H. Lund-Andersen, Diabetic macular edema assessed with optical coherence tomography and stereo fundus photography. Invest Ophthalmol Vis Sci 2002; 43: P.W. Truitt, G.S. Ogawa, M.G. Wood, S.C. Nemeth, and P. Soliz, Characterization of Normal and Abnormal Ocular Tissues Using a Hyperspectral Fundus Imaging System. In Association for Research in Vision and Ophthalmology (ARVO), May Ft. Lauderdale, FL. 9. D.A. Farnath, P.W. Truitt, S. Padilla, L.J. Otten, and P. Soliz, Characterization of Malignant Eyelid Lesions Using Hyperspectral Imaging. In Association for Research in Vision and Ophthalmology (ARVO), Ft. Lauderdale, FL. 10. N. Magotra, E. Wu, P. Soliz, and P.W. Truitt, Developing Digital Signal Processing Algorithms Hyperspectral Imaging of the Retina. In IEEE Asilomar conference on Signals, Systems and Computers Asilomar, CA. 11. L.J. Otten, P.W. Truitt, A. Meigs, P. Soliz, and I. McMackin, Hyperspectral Fundus Imager. In SPIE Imaging Spectrometers VII P.W. Truitt, S.C. Nemeth, A.D. Meigs, G.S.H. Ogawa, L.J. Otten, and P. Soliz, Hyperspectral Imaging of the Human Retina. Vision Research, 2002 (S5/1999/283). 13. P.W. Truitt, Spectral Imaging of the Human Ocular Fundus, Electrical Engineering, University of New Mexico, R.C. Gonzalez and R.E. Woods, Digital Image Processing. Addison-Wesley Publishing Company. 1993, New York, NY. 15. A. Hill, T.F. Cootes, and C.J. Taylor, Active Shape Models and the Shape Representation Approximation Problem. Image Vision Comput., : F.C. Delori and K.P. Pflibsen, Spectral reflectance of the human ocular fundus, Apl Optics 28(6), , 1989.