KEYWORDS: Clouds, Image segmentation, Prototyping, Image classification, Infrared imaging, Long wavelength infrared, Signal to noise ratio, Satellites, Thermography, Algorithm development
This paper reports on a novel approach to atmospheric cloud segmentation from a space based multi-spectral pushbroom satellite system. The satellite collects 15 spectral bands ranging from visible, 0.45 um, to long wave infa-red (IR), 10.7um. The images are radiometrically calibrated and have ground sample distances (GSD) of 5 meters for visible to very near IR bands and a GSD of 20 meters for near IR to long wave IR. The algorithm consists of a hybrid-classification system in the sense that supervised and unsupervised networks are used in conjunction. For performance evaluation, a series of numerical comparisons to human derived cloud borders were performed. A set of 33 scenes were selected to represent various climate zones with different land cover from around the world. The algorithm consisted of the following. Band separation was performed to find the band combinations which form significant separation between cloud and background classes. The potential bands are fed into a K-Means clustering algorithm in order to identify areas in the image which have similar centroids. Each cluster is then compared to the cloud and background prototypes using the Jeffries-Matusita distance. A minimum distance is found and each unknown cluster is assigned to their appropriate prototype. A classification rate of 88% was found when using one short wave IR band and one mid-wave IR band. Past investigators have reported segmentation accuracies ranging from 67% to 80%, many of which require human intervention. A sensitivity of 75% and specificity of 90% were reported as well.
Age-Related Macular Degeneration (ARMD) is the leading cause of irreversible visual loss among the elderly in the US and Europe. A computer-based system has been developed to provide the ability to track the position and margin of the ARMD associated lesion; drusen. Variations in the subject's retinal pigmentation, size and profusion of the lesions, and differences in image illumination and quality present significant challenges to most segmentation algorithms. An algorithm is presented that first classifies the image to optimize the variables of a mathematical morphology algorithm. A binary image is found by applying Otsu's method to the reconstructed image. Lesion size and area distribution statistics are then calculated. For training and validation, the University of Wisconsin provided longitudinal images of 22 subjects from their 10 year Beaver Dam Study. Using the Wisconsin Age-Related Maculopathy Grading System, three graders classified the retinal images according to drusen size and area of involvement. The percentages within the acceptable error between the three graders and the computer are as follows: Grader-A: Area: 84% Size: 81%; Grader-B: Area: 63% Size: 76%; Grader-C: Area: 81% Size: 88%. To validate the segmented position and boundary one grader was asked to digitally outline the drusen boundary. The average accuracy based on sensitivity and specificity was 0.87 for thirty four marked regions.
The fusion of multi-modal medical images provides a new diagnostic tool with clinical applications. Over the years, image fusion has been used in a number of medical disciplines. However, little fusion work in ophthalmic imaging appears in the literature. With the advent of multi-modal digital information of the retina and advanced image registration programs, the possibility of displaying complementary information in one fused retinal image becomes visually and clinically exciting. The objective of this research was to demonstrate that through fusion of multi-modal retinal information one could increase the information content of retinal pathologies on a fused image. Two aspects of image fusion were addressed in this study: image registration and image fusion of two distinctly different modalities, Fluorescein Angiography (FA) videos and standard color photography. Quantitative analysis of the fusion results was performed using entropy and image noise index. Qualitative analysis was performed by simultaneous visual comparison of two modalities (FA and color) of all registered unfused modes and the fused modes.
Feature extraction is a critical preprocessing step, which influences the outcome of the entire process of developing significant metrics for medical image evaluation. The purpose of this paper is firstly to compare the effect of an optimized statistical feature extraction methodology to a well designed combination of point operations for feature extraction at the preprocessing stage of retinal images for developing useful diagnostic metrics for retinal diseases such as glaucoma and diabetic retinopathy. Segmentation of the extracted features allow us to investigate the effect of occlusion induced by these features on generating stereo disparity mapping and 3-D visualization of the optic cup/disc. Segmentation of blood vessels in the retina also has significant application in generating precise vessel diameter metrics in vascular diseases such as hypertension and diabetic retinopathy for monitoring progression of retinal diseases.
This paper presents the results from applying a computer- based methodology for making precise measurements of longitudinal changes in a patient's digital retinal images presenting with age-related macular degeneration. The digital retinal image analysis system applies recognized principles in automatic image segmentation and integrates the automation with a graphical user interface. Drusen, retinal lesions associated with age-related macular degeneration (ARMD), were segmented using a region-growing algorithm. The algorithm calculates the 76 percentile intensity in a region to provide seed points for the neighborhood-growing algorithm. Twenty-one cases were analyzed. Agreement statistics (kappa) were determined by comparing the automated results with those provided from manually derived measurements. Agreement statistics ranged from 0.49 to 0.71 for different regions of the retina. The manual analysis ground truth was performed by trained graders from the University of Wisconsin Reading Center using guidelines found in the Wisconsin Age-Related Maculopathy Degeneration Grading Scheme (WARMGS). Because of the time required, the ophthalmic graders can only grade (size, area, type) the most prominent drusen in specific regions, resulting in a small sampling of drusen lesions in the retina. The computer-based approach allows one to efficiently and comprehensively grade all of the lesions for larger numbers of images. The additional advantage, however, is in the precision and total area that can be graded with the computer-aided technology. Computer-registered longitudinal images produced a precise determination of the temporal changes in the individual lesions. This study has demonstrated a robust segmentation and registration methodology for automatic and semiautomatic detection and measurement of abnormal regions in longitudinal retinal images.
This paper describes an automated 3-D surface recovery algorithm for consistent and quantitative evaluation of the deformation in the ONH (optic nerve head). Additional measures, such as the changes in the volume of the cup and the disc as an improvement to the traditional cup to disc ratios, can thus be developed for longitudinal follow-up study of a patient. We propose an automated computerized technique for stereo pair registration and surface visualization of the ONH. Power cepstrum and zero mean cross correlation are embedded in the registration and a 3-D surface recovery technique is proposed. Preprocessing, as well as an overall registration, is performed upon stereo pairs. Then a coarse to fine feature matching strategy is used to reduce the ambiguity in finding the conjugate pair of the same point within the constraints of the epipolar plane. A cubic B-spline interpolation smooths the representation of the ONH obtained, while superimposition of features such as blood vessels is added. Studies show high correlation between traditional cup/disc measures derived from manual segmentation by ophthalmologists and computer generated cup/disc volume ratio. Such longitudinal studies over a large population of glaucoma patients are currently in progress for validation of the surface recovery algorithm.
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