SEGMENTASI MICROANEURYSM PADA CITRA FUNDUS RETINA UNTUK DETEKSI DINI DIABETIC RETINOPATHY

Abstrak. Diabetik Retinopathy(DR) merupakan kelainan retina akibat dari komplikasi diabetesyang menyebabkan kebutaan. kondisi ini yang dikenal sebagai diabetik retinopati. Salah satucara untuk mengetahui bahwa ada diabetik retinopati dapat dilihat dari adanya kemunculanmicroaneurysm pada retina yang bisa dilihat melalui alat kedokteran kamera fundus. Penelitian inimengajukan suatu langkah untuk segmentasi microaneurysm pada citra retina untuk deteksi secaradini diabetik retinopati. Tahaan dalam penelitian ini adalah me-resize ulang citra, transformasi rgbke red channel, rekonstruksi morfologi, kemudian memperbaiki citra tersebut dengan contrastenhancement. Microaneurysm (MA) dideteksi dengan menggunakan filter Laplacian of gaussian.Area MA diperjelas dengan menggunakan tophat filtering, kemudian tahap terakhir dalamsegmentasi menggunakan thresholding.Kata Kunci: Diabetik Retinopati,citra fundus,deteksi microaneurysm,metode morfologi,Laplacianof gaussian, thresholding

Microaneurysms Segmentation in Retinal Images for Early Detection of Diabetic Retinopathy

Microaneurysms (MAs) are the tiny aneurysms which show the earliest sign of diabetic retinopathy (DR). MAs might progress and harm human eyes if not treated. This paper presents an automatic method for segmentation of MAs in order to control the progression of DR. MESSIDOR database of 40 random images were utilised for further processing. The proposed approach covered pre-processing steps, contrast enhancement, filtration and segmentation by h-maxima transform and multilevel thresholding. Some post-processing techniques also involved in this approach using morphological operation. The detected MAs determined the grade of disease severity. The result showed that the percentage of severity disease detected was 60%

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

The automated detection of proliferative diabetic retinopathy using dual ensemble classification

Objective: Diabetic retinopathy (DR) is a retinal vascular disease that is caused by complications of diabetes. Proliferative diabetic retinopathy (PDR) is the advanced stage of the disease which carries a high risk of severe visual impairment. This stage is characterized by the growth of abnormal new vessels. We aim to develop a method for the automated detection of new vessels from retinal images. Methods: This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel maps which each hold vital information. Local morphology, gradient and intensity features are measured using each binary vessel map to produce two separate 21-D feature vectors. Independent classification is performed for each feature vector using an ensemble system of bagged decision trees. These two independent outcomes are then combined to a produce a final decision. Results: Sensitivity and specificity results using a dataset of 60 images are 1.0000 and 0.9500 on a per image basis. Conclusions: The described automated system is capable of detecting the presence of new vessels

Detection of microaneurysms in retinal images using an ensemble classifier

This paper introduces, and reports on the performance of, a novel combination of algorithms for automated microaneurysm (MA) detection in retinal images. The presence of MAs in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR) which is one of the leading causes of blindness amongst the working age population. An extensive survey of the literature is presented and current techniques in the field are summarised. The proposed technique first detects an initial set of candidates using a Gaussian Matched Filter and then classifies this set to reduce the number of false positives. A Tree Ensemble classifier is used with a set of 70 features (the most commons features in the literature). A new set of 32 MA groundtruth images (with a total of 256 labelled MAs) based on images from the MESSIDOR dataset is introduced as a public dataset for benchmarking MA detection algorithms. We evaluate our algorithm on this dataset as well as another public dataset (DIARETDB1 v2.1) and compare it against the best available alternative. Results show that the proposed classifier is superior in terms of eliminating false positive MA detection from the initial set of candidates. The proposed method achieves an ROC score of 0.415 compared to 0.2636 achieved by the best available technique. Furthermore, results show that the classifier model maintains consistent performance across datasets, illustrating the generalisability of the classifier and that overfitting does not occur

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis