Name
Abstract | ||
|---|---|---|
Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available Diaret DB1 database are conducted to verify the effectiveness of our proposedmethod. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/9854825 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Field | DocType | Volume |
Diabetic retinopathy,Computer vision,Computer science,Artificial intelligence,Classifier (linguistics) | Journal | 2017 |
ISSN | Citations | PageRank |
1748-670X | 0 | 0.34 |
References | Authors | |
7 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Zhou | 1 | 8 | 2.11 |
| Chengdong Wu | 2 | 250 | 46.36 |
| Dali Chen | 3 | 60 | 6.10 |
| Zhenzhu Wang | 4 | 4 | 0.78 |
| Yugen Yi | 5 | 92 | 15.25 |
| Wenyou Du | 6 | 6 | 1.14 |