Retinal fundus görüntülerde kan damarı bölütleme işlemi, diyabetik retinopati, glukoma gibi bazı hastalıkların teşhisi ve ön tanısı için önemli bir aşamadır. Bu çalışmada renkli retinal fundus görüntülerde damar bölütleme amacıyla kullanılan denetimli sınıflandırma yöntemleri uygulamalı olarak karşılaştırılmaktadır. Sınıflandırma işleminden önce kan damarı ve retina arkaplan piksellerini birbirinden ayıracak şekilde damar iyileştirmeye dayalı piksel tabanlı özellik çıkarma işlemi gerçekleştirilir. Daha sonra çıkarılan bu özellikler kullanılarak sınıflandırıcı yardımıyla piksellerin kan damarına ya da arkaplana ait olup olmadığına karar verilir. Denetimli sınıflandırma yöntemi olarak k en yakın komşuluk, Naive Bayes sınıflandırıcı ve destek vektör makinaları kullanılmaktadır. Performans değerlendirmesi için internet üzerinde erişilebilir olan STARE ve DRIVE veritabanları kullanılmaktadır. Sonuç olarak elde edilen başarım değerleri ve işlem süreleri karşılaştırılmıştır. Naive Bayes sınıflandırma yönteminin en hızlı ve destek vektör makinarı yönteminin ise diğerlerine göre daha yüksek başarı sağladığı gözlenmiştir.
Denetimli sınıflandırma k en yakın komşular Naive Bayes destek vektör makineleri öznitelik çıkarma damar bölütleme kan damarı iyileştirme
Blood vessel segmentation in retinal fundus images is the first step for the diagnosis and treatment of diseases such as diabetic retinopathy, glaucoma and age related macular degeneration. In this paper, several supervised classification methods with adapted features are used in order to extract blood vessels in color retinal fundus images. Furthermore, the obtained results are compared against each other in terms of computational durations and classification accuracy. Firstly, a pixel based feature extraction method is performed in which features are extracted from the enhanced images of the vessel. Afterwards, a classification stage is performed to decide whether a pixel belongs to a vessel or the retinal background using these features. K-nearest neighbors, Naïve Bayes and support vector machines are used as supervised classification mechanisms. Retinal fundus images from two publicly available database STARE and DRIVE are used for performance evaluation. Obtained performance values and computation time results are compared. As a result, it is observed that Naïve Bayes classifier is the fastest method and support vector machines method has the highest accuracy.
Supervised classification k nearest neighbors Naive Bayes support vector machines feature extraction blood vessel enhancement vessel segmentation
Other ID | JA37NE57TJ |
---|---|
Journal Section | Makaleler(Araştırma) |
Authors | |
Publication Date | June 24, 2016 |
Published in Issue | Year 2015 Volume: 8 Issue: 1 |
Article Acceptance
Use user registration/login to upload articles online.
The acceptance process of the articles sent to the journal consists of the following stages:
1. Each submitted article is sent to at least two referees at the first stage.
2. Referee appointments are made by the journal editors. There are approximately 200 referees in the referee pool of the journal and these referees are classified according to their areas of interest. Each referee is sent an article on the subject he is interested in. The selection of the arbitrator is done in a way that does not cause any conflict of interest.
3. In the articles sent to the referees, the names of the authors are closed.
4. Referees are explained how to evaluate an article and are asked to fill in the evaluation form shown below.
5. The articles in which two referees give positive opinion are subjected to similarity review by the editors. The similarity in the articles is expected to be less than 25%.
6. A paper that has passed all stages is reviewed by the editor in terms of language and presentation, and necessary corrections and improvements are made. If necessary, the authors are notified of the situation.
. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.