Peter Soliz

Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 × 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.

Fast Localization and Segmentation of Optic Disk in Retinal Images Using Directional Matched Filtering and Level Sets

The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.

Digital stereo image analyzer for generating automated 3-D measures of optic disc deformation in glaucoma

The major limitations of precise evaluation of retinal structures in present clinical situations are the lack of standardization, the inherent subjectivity involved in the interpretation of retinal images, and intra- as well as interobserver variability. While evaluating optic disc deformation in glaucoma, these limitations could be overcome by using advanced digital image analysis techniques to generate precise metrics; from stereo optic disc image pairs. A digital stereovision system for visualizing the topography of the optic nerve head from stereo optic disc images is presented. We have developed an algorithm, combining power cepstrum and zero-mean-normalized cross correlation techniques, which extracts depth information using coarse-to-fine disparity between corresponding windows in a stereo pair. The gray level encoded sparse disparity matrix is subjected to a cubic B-spline operation to generate smooth representations of the optic cup/disc surfaces and new three-dimensional (3-D) metrics from isodisparity contours. Despite the challenges involved in 3-D surface recovery, the robustness of our algorithm in finding disparities within the constraints used has been validated using stereo pairs with known disparities. In a preliminary longitudinal study of glaucoma patients, a strong correlation is found between the computer-generated quantitative cup/disc volume metrics and manual metrics commonly used in a clinic. The computer generated new metrics, however, eliminate the subjective variability and greatly reduce the time and cost involved in manual metric generation in follow-up studies of glaucoma.

Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images

PURPOSE To describe and evaluate the performance of an algorithm that automatically classifies images with pathologic features commonly found in diabetic retinopathy (DR) and age-related macular degeneration (AMD). METHODS Retinal digital photographs (N = 2247) of three fields of view (FOV) were obtained of the eyes of 822 patients at two centers: The Retina Institute of South Texas (RIST, San Antonio, TX) and The University of Texas Health Science Center San Antonio (UTHSCSA). Ground truth was provided for the presence of pathologic conditions, including microaneurysms, hemorrhages, exudates, neovascularization in the optic disc and elsewhere, drusen, abnormal pigmentation, and geographic atrophy. The algorithm was used to report on the presence or absence of disease. A detection threshold was applied to obtain different values of sensitivity and specificity with respect to ground truth and to construct a receiver operating characteristic (ROC) curve. RESULTS The system achieved an average area under the ROC curve (AUC) of 0.89 for detection of DR and of 0.92 for detection of sight-threatening DR (STDR). With a fixed specificity of 0.50, the systems sensitivity ranged from 0.92 for all DR cases to 1.00 for clinically significant macular edema (CSME). CONCLUSIONS A computer-aided algorithm was trained to detect different types of pathologic retinal conditions. The cases of hard exudates within 1 disc diameter (DD) of the fovea (surrogate for CSME) were detected with very high accuracy (sensitivity = 1, specificity = 0.50), whereas mild nonproliferative DR was the most challenging condition (sensitivity = 0.92, specificity = 0.50). The algorithm was also tested on images with signs of AMD, achieving a performance of AUC of 0.84 (sensitivity = 0.94, specificity = 0.50).

Vision-based, real-time retinal image quality assessment

Real-time medical image quality is a critical requirement in a number of healthcare environments, including ophthalmology where studies suffer loss of data due to unusable (ungradeable) retinal images. Several published reports indicate that from 10% to 15% of images are rejected from studies due to image quality. With the transition of retinal photography to lesser trained individuals in clinics, image quality will suffer unless there is a means to assess the quality of an image in real-time and give the photographer recommendations for correcting technical errors in the acquisition of the photograph. The purpose of this research was to develop and test a methodology for evaluating a digital image from a fundus camera in real-time and giving the operator feedback as to the quality of the image. By providing real-time feedback to the photographer, corrective actions can be taken and loss of data or inconvenience to the patient eliminated. The methodology was tested against image quality as perceived by the ophthalmologist. We successfully applied our methodology on over 2,000 images from four different cameras acquired through dilated and undilated imaging conditions. We showed that the technique was equally effective on uncompressed and compressed (JPEG) images. We achieved a 100 percent sensitivity and 96 percent specificity in identifying “rejected” images.

Stimulus-Evoked Intrinsic Optical Signals in the Retina: Spatial and Temporal Characteristics

PURPOSE To characterize the properties of stimulus-evoked retinal intrinsic signals and determine the underlying origins. METHODS Seven adult cats were anesthetized and paralyzed to maximize imaging stability. The retina was stimulated with a liquid crystal display (LCD) integrated into a modified fundus camera (Topcon, Tokyo, Japan). The LCD presented patterned visual stimuli while the retina was illuminated with near infrared (NIR) light. The peristimulus changes in the NIR reflectance of the retina were recorded with a digital camera. RESULTS Two stimulus-evoked reflectance signals in the NIR were observed: a positive signal, corresponding to a relative increase in reflectance, and a negative signal, corresponding to a relative decrease in reflectance. When presented with a positive-contrast stimulus, the negative reflectance signals showed a tight spatial coupling with the stimulated region of retina, whereas the positive signals arose in an adjacent region of the retina. Signals remained spatially confined to the stimulated region even when stimuli of much longer duration were used. In addition, the positive and negative signal polarities reversed when the stimulus contrast was inverted. Both signals showed a rise time on the order of seconds, similar to those observed in the mammalian neocortex. The spectral dependency of the signals on illumination was similar to the absorbance spectra of hemoglobin and the oximetric relationship. CONCLUSIONS The findings characterize the basic properties of stimulus-evoked intrinsic signals of the retina. These signals were generally similar to the more extensively studied cortical signals. Collectively, the data suggest a hemodynamic component to the intrinsic optical signals of the retina.

Detection of neovascularization in the optic disc using an AM-FM representation, granulometry, and vessel segmentation

Neovascularization, defined as abnormal formation of blood vessels in the retina, is a sight-threatening condition indicative of late-stage diabetic retinopathy (DR). Ischemia due to leakage of blood vessels causes the body to produce new and weak vessels that can lead to complications such as vitreous hemorrhages. Neovascularization on the disc (NVD) is diagnosed when new vessels are located within one disc-diameter of the optic disc. Accurately detecting NVD is important in preventing vision loss due to DR. This paper presents a method for detecting NVD in digital fundus images. First, a region of interest (ROI) containing the optic disc is manually selected from the image. By adaptively combining contrast enhancement methods with a vessel segmentation technique, the ROI is reduced to the regions indicated by the segmented vessels. Textural features extracted by using amplitude-modulation frequency-modulation (AM-FM) techniques and granulometry are used to differentiate NVD from a normal optic disc. Partial least squares is used to perform the final classification. Leave-one-out cross-validation was used to evaluate the performance of the system with 27 NVD and 30 normal cases. We obtained an area under the receiver operator characteristic curve (AUC) of 0.85 by using all features, increasing to 0.94 with feature selection.

Multi-scale AM-FM for lesion phenotyping on age-related macular degeneration

Age-related macular degeneration (AMD) is the most common cause of visual loss in the United States and is a growing public health problem. The presence and severity of AMD in current epidemiological studies is detected by the grading of color stereoscopic fundus photographs. The purpose of this study was to show that a mathematical technique, amplitude-modulation frequency modulation (AM-FM) can be used to generate multi-scale features for classifying pathological structures, such as drusen, on a retinal image. AM-FM features were calculated for N=120 40×40 regions from 5 retinal images presenting with age-related macular degeneration. The results show that with this technique, drusen can be differenced from normal retinal structures by more than three standard deviations using the AM-FM histograms. In addition, by using different color spaces highly accurate classification of structures of the retina is achieved. These results are the first step in the development of an automated AMD grading system.

Computer-aided methods for quantitative assessment of longitudinal changes in retinal images presenting with maculopathy

This paper presents the results from applying a computer- based methodology for making precise measurements of longitudinal changes in a patients 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.

Automated Analysis of Optic Nerve Images for Detection and Staging of Papilledema

PURPOSE To develop an automated system that analyzes digital fundus images for staging and monitoring of optic disc edema (i.e., papilledema), due to raised intracranial pressure. METHODS A total of 294 retrospective, digital photographs of the right and left eyes of 39 subjects with papilledema acquired over the span of 2 years were used. Software tools were developed to analyze three features of papilledema from digital fundus photographs: (1) sharpness of the optic disc border, (2) discontinuity along major vessels overlying the optic nerve, and (3) texture properties of the peripapillary retinal nerve fiber layer (RNFL). A classifier used these features to assign a grade of papilledema according to a standard protocol used by an expert neuro-ophthalmologist (RK). RESULTS The algorithm showed substantial agreement (κ = 0.71, P < 0.001) with the neuro-ophthalmologist when grading papilledema per patient. Vessel features showed statistical significance (P < 0.05) in differentiating grades 0, 1, and 2 from grades 3 and 4, whereas disc obscuration differentiated grades 0 or 1 from the rest (P < 0.05). CONCLUSIONS These results show that this algorithm can be used to automatically grade papilledema. The algorithm provides objective and quantitative assessment of the stage of papilledema with accuracy that is comparable to grading by a neuro-ophthalmologist. One application is in rapid assessment of digital optic nerve photographs acquired in clinical, intensive care, and emergency response settings by nonophthalmologists to evaluate for the presence and severity of papilledema, due to intracranial hypertension.