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Giancardo, Luca

Publications and source records attributed to Giancardo, Luca.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Retina Image Analysis and Ocular Telehealth: The Oak Ridge National Laboratory-Hamilton Eye Institute Case Study

Automated retina image analysis has reached a high level of maturity in recent years, and thus the question of how validation is performed in these systems is beginning to grow in importance. One application of retina image analysis is in telemedicine, where an automated system could enable the automated detection of diabetic retinopathy and other eye diseases as a low-cost method for broad-based screening. In this work we discuss our experiences in developing a telemedical network for retina image analysis, including our progression from a manual diagnosis network to a more fully automated one. We pay special attention to how validations of our algorithm steps are performed, both using data from the telemedicine network and other public databases.

Karnowski, Thomas↗
DOE OSTI2011

Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Health Insurance Portability and Accountability Act-Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Statistical Characterization and Segmentation of Drusen in Fundus Images

Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach to segment drusen with the ultimate goal of characterizing the AMD progression in a data set of longitudinal images. The method characterizes retinal structures with a statistical model of the colors in the retina image. When comparing the segmentation results of the method between longitudinal images with known AMD progression and those without, the method detects progression in our longitudinal data set with an area under the receiver operating characteristics curve of 0.99.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Exudate-based diabetic macular edema detection in fundus images using publicly available datasets

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4 s (9.3 s, considering the optic nerve localization) per image on an 2.6 GHz platform with an unoptimized Matlab implementation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Quality Enhancement and Nerve Fibre Layer Artefacts Removal in Retina Fundus Images by Off Axis Imaging

Retinal fundus images acquired with non-mydriatic digital fundus cameras are a versatile tool for the diagnosis of various retinal diseases. Because of the ease of use of newer camera models and their relative low cost, these cameras are employed worldwide by retina specialists to diagnose diabetic retinopathy and other degenerative diseases. Even with relative ease of use, the images produced by these systems sometimes suffer from reflectance artefacts mainly due to the nerve fibre layer (NFL) or other camera lens related reflections.We propose a technique that employs multiple fundus images acquired from the same patient to obtain a single higher quality image without these reflectance artefacts. The removal of bright artefacts, and particularly of NFL reflectance, can have great benefits for the reduction of false positives in the detection of retinal lesions such as exudate, drusens and cotton wool spots by automatic systems or manual inspection. If enough redundant information is provided by the multiple images, this technique also compensates for a suboptimal illumination.The fundus images are acquired in straightforward but unorthodox manner, i.e. the stare point of the patient is changed between each shot but the camera is kept fixed. Between each shot, the apparent shape and position of all the retinal structures that do not exhibit isotropic reflectance (e.g. bright artefacts) change. This physical effect is exploited by our algorithm in order to extract the pixels belonging to the inner layers of the retina, hence obtaining a single artefacts-free image.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Combining Image and Non-Image Data for Automatic Detection of Retina Disease in a Telemedicine Network

A telemedicine network with retina cameras and automated quality control, physiological feature location, and lesion/anomaly detection is a low-cost way of achieving broad-based screening for diabetic retinopathy (DR) and other eye diseases. In the process of a routine eye-screening examination, other non-image data is often available which may be useful in automated diagnosis of disease. In this work, we report on the results of combining this non-image data with image data, using the protocol and processing steps of a prototype system for automated disease diagnosis of retina examinations from a telemedicine network. The system includes quality assessments, automated physiology detection, and automated lesion detection to create an archive of known cases. Non-image data such as diabetes onset date and hemoglobin A1c (HgA1c) for each patient examination are included as well, and the system is used to create a content-based image retrieval engine capable of automated diagnosis of disease into 'normal' and 'abnormal' categories. The system achieves a sensitivity and specificity of 91.2% and 71.6% using hold-one-out validation testing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step and therefore do not require ground-truthed lesion training sets which are time consuming to create, difficult to obtain, and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.

59 BASIC BIOLOGICAL SCIENCES↗