Extraction of Microaneurysms and Hemorrhages from Digital Retinal Images

Mamilla, Ravindranath Tagore; Ede, Venkata Krishna Rao; Bhima, Prabhakar Rao

doi:10.1007/s40846-017-0237-1

Cited by 11 publications

(6 citation statements)

References 30 publications

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“…It is possible to categorize the existing methods for abnormality detection in retinal fundus images into mathematical morphology-based 26 – 29 , region growing-based 30 , 31 , wavelet-based 32 – 34 , pixel classification 17 , artificial intelligence and deep learning 35 , 36 , knowledge-based 37 and hybrid approaches 38 – 41 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…With respect to the mathematical morphology-based methods, a red-lesion detection and classification method was proposed in Ref. 26 . This method consists of four steps.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The methods focused on detecting abnormalities in retinal fundus images can be categorized into mathematical morphology-based 26 – 29 , region growing-based 30 , 31 , wavelet-based 32 – 34 , pixel classification 17 , artificial intelligence and deep learning 35 , 36 , knowledge-based 37 and hybrid approaches 38 – 41 . In the next section, each class is described by explaining sample related research works.…”

Section: Introductionmentioning

confidence: 99%

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Red-lesion extraction in retinal fundus images by directional intensity changes’ analysis

Monemian

Rabbani

2021

Sci Rep

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Diabetic retinopathy (DR) is an important retinal disease threatening people with the long diabetic history. Blood leakage in retina leads to the formation of red lesions in retina the analysis of which is helpful in the determination of severity of disease. In this paper, a novel red-lesion extraction method is proposed. The new method firstly determines the boundary pixels of blood vessel and red lesions. Then, it determines the distinguishing features of boundary pixels of red-lesions to discriminate them from other boundary pixels. The main point utilized here is that a red lesion can be observed as significant intensity changes in almost all directions in the fundus image. This can be feasible through considering special neighborhood windows around the extracted boundary pixels. The performance of the proposed method has been evaluated for three different datasets including Diaretdb0, Diaretdb1 and Kaggle datasets. It is shown that the method is capable of providing the values of 0.87 and 0.88 for sensitivity and specificity of Diaretdb1, 0.89 and 0.9 for sensitivity and specificity of Diaretdb0, 0.82 and 0.9 for sensitivity and specificity of Kaggle. Also, the proposed method has a time-efficient performance in the red-lesion extraction process.

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Section: Literature Reviewmentioning

confidence: 99%

“…With respect to the mathematical morphology-based methods, a red-lesion detection and classification method was proposed in Ref. 26 . This method consists of four steps.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Red-lesion extraction in retinal fundus images by directional intensity changes’ analysis

Monemian

Rabbani

2021

Sci Rep

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“…Different techniques have been used for the detection of hemorrhages in retinal images. Mamilla et al [2] presented a new technique to extract small red dots based on phase congruency and mathematical morphology. Adem et al [3] proposed a technique to extract dark regions by iterative thresholding approach based on firefly and particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Hemorrhages in Retinal Images

Ali¹,

Sariera²,

Akram³

et al. 2023

Computer Systems Science and Engineering

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Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy (DR). Hemorrhages is the first clinically visible symptoms of DR. This paper presents a new technique to extract and classify the hemorrhages in fundus images. The normal objects such as blood vessels, fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages. For masking blood vessels, thresholding that separates blood vessels and background intensity followed by a new filter to extract the border of vessels based on orientations of vessels are used. For masking optic disc, the image is divided into subimages then the brightest window with maximum variance in intensity is selected. Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques. Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features. Three different types of Support Vector Machine (SVM), Linear SVM, Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemorrhages or healthy. The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico. The performance of the method is measured based on average sensitivity, specificity, F-score and accuracy. Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.

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“…Mamilla et al 2017 [12] proposed a coarse-tofine strategy for detection of red lesions, it is based on combination of phase congruency and mathematical morphology to extract candidates of red lesions as the coarse stage. The computation of phase congruency is achieved by using extended 2D log gabor filter.…”

Section: Introductionmentioning

confidence: 99%

Automated Approach for Extraction of Microaneurysms and Hemorrhages in Retinal Fundus Images

Jaafar¹,

Al-Libawy²,

Al-Gayem³

2020

IJIES

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Early detection and quantification of microaneurysms and hemorrhages can potentially help reduce the risk of vision loss. In this paper, an automatic algorithm for extraction of microaneurysms and hemorrhages from color fundus images is presented. The color retinal image is analyzed and segmented using a top-down segmentation after some image enhancement steps, namely shade correction and contrast enhancement. The whole segmented images create a dataset of regions. Edge detection followed by region-growing techniques are also used to refine the top-down segmentation results. Segmented regions could be classified into true lesions and artifacts using a set of prior features like size, edge strength, color and texture. These are extracted and then utilized with a local-region properties classifier. To validate the outcomes of the proposed method, clinician's references were used to calculate the average performance measures. The proposed method achieved sensitivity of 98.8%, specificity of 97.7%, accuracy of 99.3%, and positive predictive value of 83.7% per-pixel basis. Superior performance measures and high computation speed have assured that the proposed method is more efficient and reproducible compared to the manual methods. The significant novelty of this work is in the use of three refining operations, i.e. Edge detection, region growing and classification to achieve optimal performance especially for the sensitivity and positive predictive value. The performance measures of the proposed algorithm are compared against many recent automated systems and are found to outperform most of them. The proposed algorithm performance is very close to that of the specialist ground truth. Hence, premium performance and high computation speed of this algorithm make it promising approach for a computer-aided mass screening of retinal diseases.

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Extraction of Microaneurysms and Hemorrhages from Digital Retinal Images

Cited by 11 publications

References 30 publications

Red-lesion extraction in retinal fundus images by directional intensity changes’ analysis

Red-lesion extraction in retinal fundus images by directional intensity changes’ analysis

Detection and Classification of Hemorrhages in Retinal Images

Automated Approach for Extraction of Microaneurysms and Hemorrhages in Retinal Fundus Images

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