DETECTION OF HARD EXUDATES IN DIABETIC RETINOPATHY RETINAL IMAGES BY UTILIZING VISUAL DICTIONARY AND CLASSIFIER APPROACHES

Yıl 2016, , 1 - 6, 08.06.2016

Kemal Akyol , Şafak Bayır , Baha Şen

https://doi.org/10.22531/muglajsci.269964

Cited By: 1

Öz

Diabetic retinopathy is a disease that causes blindness resulting from damages that emerge in the retina depending on the diabetes mellitus. There are two stages of the disease including the non-proliferative and proliferative. Eyesight loss is blocked by means of early detection and diagnosis of non-proliferative DR findings. In this study, we designed a decision support system for automatic detection of hard exudates which are early stage DR lesions. This system consists of region-of-interest, feature extraction, visual dictionary and classifying stages. We tested the performance of the system, which we carried out based on system learning and analysis of new retinal images, on the public DIARETDB1 retinal image dataset. Experimental results showed us that machine learning technique suggested by us is successful.

Anahtar Kelimeler

Hard exudate, Keypoint extraction, Feature extraction, Visual dictionary, Classification

GÖRSEL SÖZLÜK VE SINIFLANDIRMA YAKLAŞIMLARINDAN FAYDALANARAK DİYABETİK RETİNOPATİLİ RETİNAL GÖRÜNTÜLERDE SERT EKSUDALARIN TESPİTİ

Öz

Diyabetik retinopati,
şeker hastalığına bağlı olarak retinada ortaya çıkan hasarlanmaların sonucu
körlüğe neden olan bir hastalıktır. Bu hastalığın erken evre (nonproliferatif) ve
ileri evre (proliferative) olmak üzere iki aşaması vardır. Erken evre DR
bulgularının erken tanı ve teşhisi sayesinde görme kaybının önüne geçilir. Bu
çalışmamızda erken evre DR lezyonlarından olan sert eksuda bölgelerinin
otomatik olarak tespiti için bir karar destek sistemi tasarladık. Bu sistem, anahtar
nokta çıkarımı, özellik çıkarımı, görsel sözlük ve sınıflandırma aşamalarını
içerir. Sistemin öğrenmesi ve yeni retinal görüntülerin analizi temeline
dayanarak gerçekleştirdiğimiz bu sistemin performansını publik (herkese açık)
DIARETDB1 retinal görüntü dataseti üzerinde test ettik. Yapay Sinir Ağları,
Rastgele Orman ve Karar Ağacı algoritmaları ile elde ettiğimiz deneysel
sonuçlar önerdiğimiz makina öğrenmesi tekniğinin başarılı olduğunu bize
göstermiştir.

Anahtar Kelimeler

Sert eksuda, Anahtar nokta çıkarımı, Özellik çıkarımı, Görsel sözlük, Sınıflandırma

Kaynakça

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