Show simple item record

dc.contributor.authorSeptiarini, Anindita
dc.date.accessioned2019-12-16T00:41:03Z
dc.date.available2019-12-16T00:41:03Z
dc.date.issued2018-08
dc.identifier.issn1746-8094
dc.identifier.urihttp://repository.unmul.ac.id/handle/123456789/3279
dc.description.abstracttThe presence of peripapillary atrophy (PPA) is associated with two kinds of diseases, namely glaucomaand myopia. PPA is one of the characteristics of these diseases that can be observed through retinal fundusimages. We propose an automatic detection method of PPA in retinal fundus images using statistical fea-tures 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 mayoccur in each sector. There are three features used in this method namely, standard deviation, smoothnessand third moment; they are selected using gain ratio method. The performance of the proposed methodachieves the accuracy of 0.95, 0.96, and 0.96 for three different datasets. These are obtained using 155retinal fundus images, from which training and testing data of 47 images and 108 images, respectively,are randomly selected.en_US
dc.description.sponsorshipThis research was funded byDirektorat Riset dan Pengabdian Masyarakat (DRPM) RISTEKDIKTIIndonesia No. 405/UN17.41/KL/2017en_US
dc.language.isoenen_US
dc.publisherELSEVIERen_US
dc.relation.ispartofseriesBiomedical Signal Processing and Control;
dc.subjectPeripapillary atrophy, Features extraction, Classification, Optic nerve head, Fundus imageaen_US
dc.titleAutomatic detection of peripapillary atrophy in retinal fundus imagesusing statistical featuresen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record