In the framework of computer assisted diagnosis of diabetic retinopathy, a new algorithm for detection of exudates is presented and discussed. The presence of exudates within the macular region is a main hallmark of diabetic macular edema and allows its detection with a high sensitivity. Hence, detection of exudates is an important diagnostic task, in which computer assistance may play a major role. Exudates are found using their high grey level variation, and their contours are determined by means of morphological reconstruction techniques. The detection of the optic disc is indispensable for this approach. We detect the optic disc by means of morphological filtering techniques and the watershed transformation. The algorithm has been tested on a small image data base and compared with the performance of a human grader. As a result, we obtain a mean sensitivity of 92.8% and a mean predictive value of 92.4%. Robustness with respect to changes of the parameters of the algorithm has been evaluated.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TMI.2002.806290DOI Listing

Publication Analysis

Top Keywords

diagnosis diabetic
8
detection exudates
8
optic disc
8
exudates
5
contribution image
4
image processing
4
processing diagnosis
4
diabetic retinopathy--detection
4
retinopathy--detection exudates
4
exudates color
4

Similar Publications

Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.

J Imaging Inform Med

January 2025

College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.

View Article and Find Full Text PDF

The literature has documented conflicting and inconsistent associations between muscle-to-fat ratios and metabolic diseases. Additionally, different adipose tissues can have contrasting effects, with visceral adipose tissue being identified as particularly harmful. This study aimed to explore the relationship between the ratio of the lean mass index (LMI) to the visceral fat mass index (VFMI) and cardiometabolic disorders, including dyslipidemia, hypertension, and diabetes, as previous research on this topic is lacking.

View Article and Find Full Text PDF

Tinea manuum is a superficial fungal infection affecting the hands, particularly the palms and interdigital areas. This retrospective study investigated clinical features, laboratory findings, treatment, and outcomes in patients with fungal hand infections at Siriraj Hospital between 2016 and 2020. Among 107 patients, representing 1.

View Article and Find Full Text PDF

Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and microscopy holds potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing software solutions for MALDI MSI data analysis are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries. Here, we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data.

View Article and Find Full Text PDF

Introduction: The most frequent form of diabetes in pediatric patients is polygenic autoimmune diabetes (T1D), but single-gene variants responsible for autoimmune diabetes have also been described. Both disorders share clinical features, which can lead to monogenic forms being misdiagnosed as T1D. However, correct diagnosis is crucial for therapeutic choice, prognosis and genetic counseling.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!