Automatic detection of peripapillary atrophy in retinal fundus images using statistical features
| Autor: | Reza Pulungan, Anindita Septiarini, Agus Harjoko, Retno Ekantini |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science business.industry Fundus image Glaucoma Health Informatics Pattern recognition Retinal 02 engineering and technology Fundus (eye) medicine.disease Backpropagation 030218 nuclear medicine & medical imaging 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine chemistry Signal Processing 0202 electrical engineering electronic engineering information engineering medicine Information gain ratio 020201 artificial intelligence & image processing Peripapillary atrophy Artificial intelligence business |
| Zdroj: | Biomedical Signal Processing and Control. 45:151-159 |
| ISSN: | 1746-8094 |
| Popis: | The presence of peripapillary atrophy (PPA) is associated with two kinds of diseases, namely glaucoma and myopia. PPA is one of the characteristics of these diseases that can be observed through retinal fundus images. We propose an automatic detection method of PPA in retinal fundus images using statistical features and Backpropagation Neural Network. In this research, those images are classified into two classes: no-PPA and PPA. The features are extracted from the focal areas, which capture the areas where PPA may occur in each sector. There are three features used in this method namely, standard deviation, smoothness and third moment; they are selected using gain ratio method. The performance of the proposed method achieves the accuracy of 0.95, 0.96, and 0.96 for three different datasets. These are obtained using 155 retinal fundus images, from which training and testing data of 47 images and 108 images, respectively, are randomly selected. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect