Detection of Hard Exudates Based on Morphological Feature Extraction

Joshi, Shilpa; Karule, P. T.

doi:10.13005/bpj/1366

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…Our expert ophthalmologist who created the ground truth analyzed a set of random images from DIARETDB0 and DIARETDB1 that highlighted DR. The method faces a lot of challenges: the retina color varies according to skin pigmentation and iris color [12,21], exudate lesions may appear blurrier in some locations as intra/inter variation in an image/images [22], and the spherical shape of the eye reflects on both illumination and sharpness of the fundus image. Because the main information was present in the green channel, we applied the Otsu binarization algorithm [23] after a Sobel filter on this image channel that enhanced and then eliminated the optic disc and the vessels' components present in the binary mask.…”

Section: Extracting Expected Exudatesmentioning

confidence: 99%

“…Exudate segmentation approaches are based mainly on thresholding, region growing, classification techniques, and mathematical morphology. The detection of hard exudates in Reference [12] was based on morphological features; moreover, post-processing In the machine-learning literature, many clustering methods have been presented, and each one has advantages and disadvantages. Mainly, these methods are accomplished either with crisp (hard) or fuzzy (soft) clustering.…”

Section: Introductionmentioning

confidence: 99%

“…Exudate segmentation approaches are based mainly on thresholding, region growing, classification techniques, and mathematical morphology. The detection of hard exudates in Reference [12] was based on morphological features; moreover, post-processing techniques were implemented to distinguish exudates from other bright artifacts, such as cotton-wools and the optic disc. In Reference [13], hard exudates were detected on two hundred proprietary retinal images by using four color and 25 texture features fed to the fuzzy support vector machine capable of avoiding the effect of outliers, noise, and artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Exudates as Landmarks Identified through FCM Clustering in Retinal Images

et al. 2020

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The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient’s status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively.

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Section: Extracting Expected Exudatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Exudate segmentation approaches are based mainly on thresholding, region growing, classification techniques, and mathematical morphology. The detection of hard exudates in Reference [12] was based on morphological features; moreover, post-processing techniques were implemented to distinguish exudates from other bright artifacts, such as cotton-wools and the optic disc. In Reference [13], hard exudates were detected on two hundred proprietary retinal images by using four color and 25 texture features fed to the fuzzy support vector machine capable of avoiding the effect of outliers, noise, and artifacts.…”

Section: Introductionmentioning

confidence: 99%

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Exudates as Landmarks Identified through FCM Clustering in Retinal Images

et al. 2020

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“…There are many methods for detecting or segmenting exudates. The first approach entails using image processing methods, sometimes in combination with machine learning methods, such as morphological operations 3 , fuzzy c-means clustering technique 4 , Kirsch’s edges 5 , stationary wavelets 5 , random forest algorithm 6 , bag of visual words 6 , and maximum margin SVM classifier 7 . Later works focused on deep-learning methods, mostly for image classification.…”

Section: Introductionmentioning

confidence: 99%

Support vector machine and deep-learning object detection for localisation of hard exudates

Kurilová

Goga

Oravec

et al. 2021

Sci Rep

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Hard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method for identifying and localising hard exudates in retinal images. To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted from the pre-trained ResNet-50 network. Subsequently, the remaining samples were processed using a faster R-CNN detector for detailed analysis. When evaluating all the exudates as individual objects, the SVM classifier reduced the false positive rate by 29.7% and marginally increased the false negative rate by 16.2%. When evaluating all the images, we recorded a 50% reduction in the false positive rate, without any decrease in the number of false negatives. The interim results suggested that pre-scanning the samples using the SVM prior to implementing the deep-network object detector could simultaneously improve and speed up the current hard exudates detection method, especially when there is paucity of training data.

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“…Another interesting algorithm is presented in [8]. The authors proposed an approach to hard exudate detection based on morphological feature extraction.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms

Saeed

Szymkowski

Saeed

et al. 2019

Sensors

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Hard exudates are one of the most characteristic and dangerous signs of diabetic retinopathy. They can be marked during the routine ophthalmological examination and seen in color fundus photographs (i.e., using a fundus camera). The purpose of this paper is to introduce an algorithm that can extract pathological changes (i.e., hard exudates) in diabetic retinopathy. This was a retrospective, nonrandomized study. A total of 100 photos were included in the analysis—50 sick and 50 normal eyes. Small lesions in diabetic retinopathy could be automatically diagnosed by the system with an accuracy of 98%. During the experiments, the authors used classical image processing methods such as binarization or median filtration, and data was read from the d-Eye sensor. Sixty-seven patients (39 females and 28 males with ages ranging between 50 and 64) were examined. The results have shown that the proposed solution accuracy level equals 98%. Moreover, the algorithm returns correct classification decisions for high quality images and low quality samples. Furthermore, we consider taking retina photos using mobile phones rather than fundus cameras, which is more practical. The paper presents an innovative approach. The results are introduced and the algorithm is described.

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Detection of Hard Exudates Based on Morphological Feature Extraction

Cited by 17 publications

References 18 publications

Exudates as Landmarks Identified through FCM Clustering in Retinal Images

Exudates as Landmarks Identified through FCM Clustering in Retinal Images

Support vector machine and deep-learning object detection for localisation of hard exudates

An Approach to Automatic Hard Exudate Detection in Retina Color Images by a Telemedicine System Based on the d-Eye Sensor and Image Processing Algorithms

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