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AIM To identify retinal exudates automatically from colour retinal images. METHODS The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. RESULTS The proposed system can(More)
Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are(More)
The location of the optic disc is of critical importance in retinal image analysis. In this work we improve on an approach introduced in [3] who localised an optic disc region through greylevel morphology followed by snake fitting. We propose and implement both the automatic initialisation of the snake and the application of morphology in colour space. We(More)
After segmenting candidate exudates regions in colour retinal images we present and compare two methods for their classification. The Neural Network based approach performs marginally better than the Support Vector Machine based approach, but we show that the latter are more flexible given criteria such as control of sensitivity and specificity rates. We(More)
Human enteroviruses (HEV) have been linked to hand, foot, and mouth disease (HFMD) in the Pacific and Southeast Asia for decades. Many cases of HFMD have been attributed to coxsackievirus A16 (CV-A16, CA16), based on only partial viral genome determination. Viral phenotypes are also poorly defined. Herein, we have genetically and phenotypically(More)
Retinal exudates are a characteristic feature of many retinal diseases such as Diabetic Retinopathy. We address the development of a method to quantitatively diagnose these random yellow patches in colour retinal images automatically. After a colour normalisation and contrast enhancement pre-processing step, the colour retinal image is segmented using Fuzzy(More)
Retinal exudates are typically manifested as spatially random yellow/white patches of varying sizes and shapes. They are a characteristic feature of retinal diseases such as diabetic maculopathy. An automatic method for the detection of exudate regions is introduced comprising image colour normalisation, enhancing the contrast between the objects and(More)
The location of the optic disc is of critical importance in retinal image analysis. We improve on past approaches on optic disc detection by working in colour space and also by localising accurately the optic disc boundary using an automatically initialised snake. The key issue presented is a comparison of colour spaces for performing colour morphology to(More)
Aim: To identify retinal exudates automatically from colour retinal images. Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. Results: The proposed system can(More)
BACKGROUND Recombination between strains of HIV-1 only occurs in individuals with multiple infections, and the incidence of recombinant forms implies that multiple infection is common. Most direct studies indicate that multiple infection is rare. We determined the rate of multiple infection in a longitudinal study of 58 HIV-1 positive participants from The(More)