Eduardo S. Barriga

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.

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.

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.

Recent multiscale AM-FM methods in emerging applications in medical imaging

Amplitude-modulation frequency-modulation (AM-FM) decompositions represent images using spatially-varying sinusoidal waves and their spatially-varying amplitudes. AM-FM decompositions use different scales and bandpass filters to extract the wide range of instantaneous frequencies and instantaneous amplitude components that may be present in an image. In the past few years, as the understanding of its theory advanced, AM-FM decompositions have been applied in a series of medical imaging problems ranging from ultrasound to retinal image analysis, yielding excellent results. This article summarizes the theory of AM-FM decompositions and related medical imaging applications.

Spatiotemporal Independent Component Analysis for the Detection of Functional Responses in Cat Retinal Images

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with current clinical instruments. Because current instruments require unattainable levels of patient cooperation, high sensitivity and specificity are difficult to attain. We have devised a new retinal imaging system that detects intrinsic optical signals which reflect functional changes in the retina and that do not require patient cooperation. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1%-1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods. The desired functional signal is masked by other physiological signals and by imaging system noise. In this paper, we quantify the limits of independent component analysis (ICA) for detecting the low intensity functional signal and apply ICA to 60 video sequences from experiments using an anesthetized cat whose retina is presented with different patterned stimuli. The results of the analysis show that using ICA, in principle, signal levels of 0.1% can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted.

Detection of low-amplitude in vivo intrinsic signals from an optical imager of retinal function

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with todays clinical instruments. Many of todays instruments focus on detecting changes in anatomical structures, such as the nerve fiber layer. Our device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. The functional imager uses a patterned stimulus at wavelength of 535nm. An intrinsic functional signal is collected at a near infrared wavelength. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by imaging system noise. In this paper, we analyze the video sequences from a set of 60 experiments with different patterned stimuli from cats. Using a set of statistical techniques known as Independent Component Analysis (ICA), we estimate the signals present in the videos. Through controlled simulation experiments, we quantify the limits of signal strength in order to detect the physiological signal of interest. The results of the analysis show that, in principle, signal levels of 0.1% (-30dB) can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted. The analysis of the different responses extracted from the videos can give an insight of the functional processes present during the stimulation of the retina.

Blind source separation in retinal videos

An optical imaging device of retina function (OID-RF) has been developed to measure changes in blood oxygen saturation due to neural activity resulting from visual stimulation of the photoreceptors in the human retina. The video data that are collected represent a mixture of the functional signal in response to the retinal activation and other signals from undetermined physiological activity. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1.0% of the total reflected intensity level which makes the functional signal difficult to detect by standard methods since it is masked by the other signals that are present. In this paper, we apply principal component analysis (PCA), blind source separation (BSS), using Extended Spatial Decorrelation (ESD) and independent component analysis (ICA) using the Fast-ICA algorithm to extract the functional signal from the retinal videos. The results revealed that the functional signal in a stimulated retina can be detected through the application of some of these techniques.

Multiscale AM-FM analysis of pneumoconiosis x-ray images

This paper presents a computer-aided diagnostic (CAD) system for analyzing chest radiographs based on the International Labor Organization (ILO) standards. We introduce an amplitude-modulation frequency-modulation (AM-FM) based methodology by which a computer-based system will extract AM-FM features and detect those with suspected interstitial lung diseases. For classification, we use Partial Least Squares (PLS) using a low number of extracted factors (making the system robust). We consider several different AM-FM classifiers based on extracting features from individual scales as well as a final classifier that combines results from the individuals scales. We validate our methodology on 11 standard images graded according to the ILO standard. For several scales, as well as for the combined classifier that uses information from all scales, we get excellent classification results (area under the receiver operator characteristics curve equal to 1.0) using a limited number of latent PLS factors.

Functional signal detection in retinal videos

An optical imaging device of retina function (OID-RF) has been developed to perform measurements of changes in blood perfusion due to neural activity resulting from visual stimulation of the photoreceptors in the human retina. Experiments were performed by measuring the changes in reflected long wave visible (700 nm) light from the retina caused by the retinal activation in response to a visual stimulus. The problem being addressed is that of detecting the signal from the retinal activation in the presence of noise from other sources, including the unstimulated retinal background and other unknown physiological changes. Preprocessing of the raw data was done to eliminate unwanted artifacts, such as blinking or excessive eye movement. Principal Component Analysis (PCA) was used to isolate the functional signal. The results of the analysis showed that regions of the retina that were stimulated could be detected using PCA.