Maria S. A. Suttorp-Schulten

Automatic detection of red lesions in digital color fundus photographs

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.

Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans

Contextual information plays an important role in medical image understanding. Medical experts make use of context to detect and differentiate pathologies in medical images, especially when interpreting difficult cases. The majority of computer-aided diagnosis (CAD) systems, however, employ only local information to classify candidates, without taking into account global image information or the relation of a candidate with neighboring structures. In this paper, we present a generic system for including contextual information in a CAD system. Context is described by means of high-level features based on the spatial relation between lesion candidates and surrounding anatomical landmarks and lesions of different classes (static contextual features) and lesions of the same type (dynamic contextual features). We demonstrate the added value of contextual CAD for two real-world CAD tasks: the identification of exudates and drusen in 2D retinal images and coronary calcifications in 3D computed tomography scans. Results show that in both applications contextual CAD is superior to a local CAD approach with a significant increase of the figure of merit of the Free Receiver Operating Characteristic curve from 0.84 to 0.92 and from 0.88 to 0.98 for exudates and drusen, respectively, and from 0.87 to 0.93 for coronary calcifications.

Aetiological study of the presumed ocular histoplasmosis syndrome in the Netherlands

AIM To investigate whether presumed ocular histoplasmosis syndrome in the Netherlands is caused by Histoplasma capsulatum and whether other risk factors might play a role in the pathogenesis of this syndrome. METHODS 23 patients were clinically diagnosed as having presumed ocular histoplasmosis syndrome based on the following criteria: peripapillary atrophy, punched out lesions, a macular disciform lesion or scar in one eye without vitritis. As controls, 66 sex and age matched healthy volunteers were used. Serum samples from both patients and controls were tested for the presence of antibodies againstH capsulatum, Toxoplasma gondii, Toxocara canis et cati,Ascaris sp, and for the presence of antigens of Cryptococcus neoformans. Serum samples were also tested for the presence of autoantibodies against retinal or choroidal proteins. To investigate other risk factors, patients and controls were asked to fill in a health and travel related questionnaire. Ten patients with ocular toxoplasmosis were used as a disease control group. RESULTS None of the patients with presumed ocular histoplasmosis syndrome or controls had circulating antibodies directed against H capsulatum. No risk factors could be identified and no indications for autoimmunity and no evidence for the role of the other infectious agents could be demonstrated. CONCLUSIONS In a Dutch group of patients fulfilling the criteria of a disease currently named presumed ocular histoplasmosis syndrome, no risk factors or relation with the fungus H capsulatum could be detected.

Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes: Response to Olson et al.

OBJECTIVE To evaluate the performance of a system for automated detection of diabetic retinopathy in digital retinal photographs, built from published algorithms, in a large, representative, screening population. RESEARCH DESIGN AND METHODS We conducted a retrospective analysis of 10,000 consecutive patient visits, specifically exams (four retinal photographs, two left and two right) from 5,692 unique patients from the EyeCheck diabetic retinopathy screening project imaged with three types of cameras at 10 centers. Inclusion criteria included no previous diagnosis of diabetic retinopathy, no previous visit to ophthalmologist for dilated eye exam, and both eyes photographed. One of three retinal specialists evaluated each exam as unacceptable quality, no referable retinopathy, or referable retinopathy. We then selected exams with sufficient image quality and determined presence or absence of referable retinopathy. Outcome measures included area under the receiver operating characteristic curve (number needed to miss one case [NNM]) and type of false negative. RESULTS Total area under the receiver operating characteristic curve was 0.84, and NNM was 80 at a sensitivity of 0.84 and a specificity of 0.64. At this point, 7,689 of 10,000 exams had sufficient image quality, 4,648 of 7,689 (60%) were true negatives, 59 of 7,689 (0.8%) were false negatives, 319 of 7,689 (4%) were true positives, and 2,581 of 7,689 (33%) were false positives. Twenty-seven percent of false negatives contained large hemorrhages and/or neovascularizations. CONCLUSIONS Automated detection of diabetic retinopathy using published algorithms cannot yet be recommended for clinical practice. However, performance is such that evaluation on validated, publicly available datasets should be pursued. If algorithms can be improved, such a system may in the future lead to improved prevention of blindness and vision loss in patients with diabetes.