Automatic Detection of Proliferative Diabetic Retinopathy With Hybrid Feature Extraction Based on Scale Space Analysis and Tracking

Feature extraction is a process to obtain the characteristics or features of an object where the value of the features will be used for analysis in the next process. In retinal image, extraction of blood vessels’ characteristics can be used for detection of proliferative diabetic retinopathy (PDR). Retinal blood vessels’ features can be obtained directly with segmented image and with additional spatial method. For PDR detection, we need the suitable method that can produce maximum feature representation. This paper proposed hybrid feature extraction using a scale space analysis method and tracking with Bayesian probability. The result of the retinal images classification from STARE database using soft threshold m-Mediods classifier shows the best accuracy of 98.1%

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

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

Classification of neovascularization using convolutional neural network model

Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image classification system between normal and neovascularization is here presented. The classification using Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and Retina Image Bank. The patches are made from a manual crop on the image that has been marked by experts as neovascularization. The dataset consists of 100 data patches. The test results using three scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%. The test performs using a single Graphical Processing Unit (GPU)

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening.

Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms

Automated detection of proliferative diabetic retinopathy from retinal images

Diabetic retinopathy (DR) is a retinal vascular disease associated with diabetes and it is one of the most common causes of blindness worldwide. Diabetic patients regularly attend retinal screening in which digital retinal images are captured. These images undergo thorough analysis by trained individuals, which can be a very time consuming and costly task due to the large diabetic population. Therefore, this is a field that would greatly benefit from the introduction of automated detection systems. This project aims to automatically detect proliferative diabetic retinopathy (PDR), which is the most advanced stage of the disease and poses a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. Their tortuous, convoluted and obscure appearance can make them difficult to detect. In this thesis, we present a methodology based on the novel approach of creating two different segmented vessel maps. Segmentation methods include a standard line operator approach and a novel modified line operator approach. The former targets the accurate segmentation of new vessels and the latter targets the reduction of false responses to non-vessel edges. Both generated binary vessel maps hold vital information which is processed separately using a dual classification framework. Features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. The proposed methodology, using a dataset of 60 images, achieves a sensitivity of 100.00% and a specificity of 92.50% on a per image basis and a sensitivity of 87.93% and a specificity of 94.40% on a per patch basis. The thesis also presents an investigation into the search for the most suitable features for the classification of PDR. This entails the expansion of the feature vector, followed by feature selection using a genetic algorithm based approach. This provides an improvement in results, which now stand at a sensitivity and specificity 3 of 100.00% and 97.50% respectively on a per image basis and 91.38% and 96.00% respectively on a per patch basis. A final extension to the project sees the framework of dual classification further explored, by comparing the results of dual SVM classification with dual ensemble classification. The results of the dual ensemble approach are deemed inferior, achieving a sensitivity and specificity of 100.00% and 95.00% respectively on a per image basis and 81.03% and 95.20% respectively on a per patch basis

Early and accurate detection of melanoma skin cancer using hybrid level set approach

Digital dermoscopy is used to identify cancer in skin lesions, and sun exposure is one of the leading causes of melanoma. It is crucial to distinguish between healthy skin and malignant lesions when using computerised lesion detection and classification. Lesion segmentation influences categorization accuracy and precision. This study introduces a novel way of classifying lesions. Hair filters, gel, bubbles, and specular reflection are all options. An improved levelling method is employed in an innovative method for detecting and removing cancerous hairs. The lesion is distinguished from the surrounding skin by the adaptive sigmoidal function; this function considers the severity of localised lesions. An improved technique for identifying a lesion from surrounding tissue is proposed in the article, followed by a classifier and available features that resulted in 94.40% accuracy and 93% success. According to research, the best method for selecting features and classifications can produce more accurate predictions before and during treatment. When the recommended strategy is put to the test using the Melanoma Skin Cancer Dataset, the recommended technique outperforms the alternative

PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE)

Perbedaan pigmentasi mempengaruhi me­­­­tode pengenalan pola citra retinopati di­a­betik beserta set­ting poinnya. Di­butuhkan sebuah pe­rangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pe­nge­nalan po­la citra retinopati dia­be­tik, dengan meng­gunakan citra kanal ku­ning (Yello­w), dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra ada­lah ciri rerata (Means), variasi Varians), skewness dan entropy, yang dilanjutkan de­ngan ekstraksi ciri  PCA (Principle Com­­ponent Analysis). Pada ekstraksi ci­ri PCA, Matriks hasil PCA meru­pakan ma­triks bujur sangkar, yang jumlah ko­lom­nya, sama dengan jumlah ciri. Pe­ne­li­tian menggunakan 4 ciri, dengan de­mi­­kian, terdapat 4 buah PC (Principle Com­ponent), PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari peng­gunaan pasangan PC. Tingkat aku­ra­si, dihitung dengan meng­gu­­nakan mo­del linear dari SVM. Model de­ngan akurasi tertinggi dan tercepat ada­lah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pem­be­lajaran tertinggi yaitu 100% dan waktu terce­pat, yang secara eksplisit diperli­hat­kan pada jumlah support vektor ter­kecil, yaitu 2. Pasa­ngan yang mempu­nyai ting­kat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengu­jian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah ca­ku­pan. Pelebaran daerah cakupan ke­mungkinan disebabkan oleh pemi­lihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bi­sa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks.</span