Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System

Jaya, Tri Sandhika; Dheeba, J.; Singh, Nikita

doi:10.1007/s10278-015-9793-5

Cited by 68 publications

(25 citation statements)

References 20 publications

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“…Sopharak et al used HSI color model and pixel-based classification whereas Franklin & Rajan used Lab color model and multilayer perceptron neural network for detection of exudates [6,7]. To extract the exudates, Jaya et al, first performed morphological operations and applied Hough transform for removal of OD [20]. Color and texture features are used as representatives of exudates.…”

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Screening using Machine Learning for Hierarchical Classification

Prabhu¹,

Bhoir²,

Shanbhag³

2019

IJITEE

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Diabetic Retinopathy is a consequence of prolonged unaddressed diabetes. It is currently diagnosed by the subjective clinical examination and manual grading of the fundus images by the ophthalmologists. This disease is progressive in nature. Hence early detection and treatment go a long way in helping the patients fight the dire consequences of the disease. Given the fact that number of ophthalmologists is very less as compared to the patients, a cost-effective, computer assisted, automated retina analysis system is highly desirable for the rural health care. This paper proposes an automatic Diabetic Retinopathy detection system based on the presence of bright lesions on the retina which is one of the symptoms of Diabetic Retinopathy. Initially the optic disc is removed from the fundus image as its brightness is similar to that of the bright lesions. Exudates are extracted and its various features are obtained. Later, feature based hierarchical classification is performed for detection of different stages of the disease. This method is based on the same logical steps as followed by the ophthalmologists and hence assures more accurate classification results. Two methodologies, Random Forest algorithm and Artificial Neural Network are explored and accuracy, sensitivity and specificity are evaluated at each stage of classification. The former outperformed the latter. The accuracy obtained using Random Forest are 100%, 85.71% and 87.5% and Artificial Neural network are 100%,78.5% and 66.67% for Stage 1, Stage 2 and Stage 3 respectively.

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Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Screening using Machine Learning for Hierarchical Classification

Prabhu¹,

Bhoir²,

Shanbhag³

2019

IJITEE

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“…Secondly, for attaining texture features from the area of retina, color and laws texture energy measures are performed. Afterwards, an intelligent classifier Fuzzy SVM has been utilized to discover pathological regions in color fundus images [ 47 ]. The EXs discovery technique comprises two stages: fine and rough EXs segmentation.…”

Section: Diabetic Retinopathy Detection By Computer-aided Diagnostmentioning

confidence: 99%

A Review on Recent Developments for Detection of Diabetic Retinopathy

2016

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Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area.

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“…It is firstly proposed by D.Gabor in his paper published in 1946. In order to extract the local information of Fourier transform of the signal, he has introduced a time localization window function and its parameters can be used to translate the window to cover the entire time domain (Jaya and Dheeba et al, 2015;Niehaus and Daniela et al, 2015). The main idea of Gabor transform is that different image textures usually have different central frequency and bandwidth and a group of Gabor filters can be designed with these frequencies and bandwidths to filter the texture image.…”

Section: Introductionmentioning

confidence: 99%

Image Texture Feature Extraction Based on Gabor Transform

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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As a regional feature, texture is a description of the spatial distribution of the image pixels. As texture can fully utilize the image information, it can become an important basis to describe and recognize the image. Compared with other image features, texture can take both the macro image properties and micro structure into consideration; therefore, feature has become a significant feature to be extracted in the target recognition. This paper analyzes the performance of Gabor filter, designs a multi-channel Gabor filter and selects its parameters. By extracting various local feature information (direction, phase, energy and so on) of the image with multichannel filter, this method can decompose an image into the sub-images based on different frequency directions and channels and it classifies the sub-images with the statistical feature extracted from the sub-image as the texture feature to represent different texture region features of the image. It is quite convenient to realize and it can obtain the optimal comparison effect in the spatial and frequency domains. The experiment result shows that the Gabor filter of this paper have excellent performance in analyzing the frequency and the direction information of the local regions of digital image.

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Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System

Cited by 68 publications

References 20 publications

Diabetic Retinopathy Screening using Machine Learning for Hierarchical Classification

Diabetic Retinopathy Screening using Machine Learning for Hierarchical Classification

A Review on Recent Developments for Detection of Diabetic Retinopathy

Image Texture Feature Extraction Based on Gabor Transform

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