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Exudates as Landmarks Identified through FCM Clustering in Retinal Images
| Content Provider | MDPI |
|---|---|
| Author | Hamad, Hadi Dwickat, Tahreer Tegolo, Domenico Valenti, Cesare |
| Copyright Year | 2020 |
| Description | The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient’s status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively. |
| Starting Page | 142 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11010142 |
| Journal | Applied Sciences |
| Issue Number | 1 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-25 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Artificial Intelligence Exudates Diabetic Retinopathy Segmentation Morphological Processing Fuzzy C-means Clustering Retinal Landmarks |
| Content Type | Text |
| Resource Type | Article |
