Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods

Antal, Bálint; Hajdú, András

doi:10.1016/j.patcog.2011.06.010

Cited by 48 publications

(16 citation statements)

References 13 publications

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“…The results of state-of-the-art approaches are reported in [25] and [45]. As Table 1 demonstrated, the proposed method notably improves the sensitivities of candidate MAs extraction.…”

Section: Results and Analysismentioning

confidence: 99%

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

Dai

2016

PLoS ONE

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Retinal microaneurysms (MAs) are the earliest clinically observable lesions of diabetic retinopathy. Reliable automated MAs detection is thus critical for early diagnosis of diabetic retinopathy. This paper proposes a novel method for the automated MAs detection in color fundus images based on gradient vector analysis and class imbalance classification, which is composed of two stages, i.e. candidate MAs extraction and classification. In the first stage, a candidate MAs extraction algorithm is devised by analyzing the gradient field of the image, in which a multi-scale log condition number map is computed based on the gradient vectors for vessel removal, and then the candidate MAs are localized according to the second order directional derivatives computed in different directions. Due to the complexity of fundus image, besides a small number of true MAs, there are also a large amount of non-MAs in the extracted candidates. Classifying the true MAs and the non-MAs is an extremely class imbalanced classification problem. Therefore, in the second stage, several types of features including geometry, contrast, intensity, edge, texture, region descriptors and other features are extracted from the candidate MAs and a class imbalance classifier, i.e., RUSBoost, is trained for the MAs classification. With the Retinopathy Online Challenge (ROC) criterion, the proposed method achieves an average sensitivity of 0.433 at 1/8, 1/4, 1/2, 1, 2, 4 and 8 false positives per image on the ROC database, which is comparable with the state-of-the-art approaches, and 0.321 on the DiaRetDB1 V2.1 database, which outperforms the state-of-the-art approaches.

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“…The results of state-of-the-art approaches are reported in [25] and [45]. As Table 1 demonstrated, the proposed method notably improves the sensitivities of candidate MAs extraction.…”

Section: Results and Analysismentioning

confidence: 99%

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

Dai

2016

PLoS ONE

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“…In this paper, we mainly focus on the detection of microaneurysms, which present at the earliest stage of DR and remain in the development of the disease [4]. Therefore, microaneurysms detection is necessary and vital in public DR screening programs.…”

Section: Introductionmentioning

confidence: 99%

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

Zhou

Chen

et al. 2017

Computational and Mathematical Methods in Medicine

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Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

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“…The automatic detection of red lesions in color fundus images is obtained by a number of alreasdy exist technologies. Most of the system [6], [7] The proposed automated redlesion detection system deals with automatic identification and effective prediction of red lesions. The preprocessing are obtained by filtering process called mean filter and the set of shapes are extracted by morphological watershed operation.…”

Section: Introduction Image Processing Is the Technique To Processmentioning

confidence: 99%

A Hybrid Approach of Detection of Glaucoma with Optic Disk Segmentation and Microaneurysm Detection in Retinal Fundus Images

Abisha¹,

Fred²,

Shobhana³

et al. 2017

IJERT

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Diabetes is the root cause for the visual problems of human, the medical treatment is highly efficient in protecting the vision loss for the diabetic patients. Vision test are encouraged for the patients on regular basis for early detection of vision problems. Automated diabetic retinopathy evaluation programs have been developed for the identification of patients with vision losing diabetic eye disease. The non diabetic persons are also encouraged to obtain the evaluation of eye for the prevention of blindness. The proposed system is an automatic detection and evaluation of the eye disease. The detection of micro aneurysms and hemorrhages in retinal image are the base for the automated classification of red lesion. It includes the following contributions a) Extraction of a set of Dynamic shape features b) Classification process for the differentiation of lesions and vessel segments. This method is examined with the fundus images in e-ophtha dataset obtained from the freely availed database called ADCIS. The preprocessing of input images was done by mean filter. The Morphological flooding is applied for the extraction of shape features. Finally, the classification was done using Random Forest (RF) classifier. The experimental results on dataset validate the efficiency of the proposed method in the detection of redlesions.

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Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods

Cited by 48 publications

References 13 publications

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

Retinal Microaneurysms Detection Using Gradient Vector Analysis and Class Imbalance Classification

Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

A Hybrid Approach of Detection of Glaucoma with Optic Disk Segmentation and Microaneurysm Detection in Retinal Fundus Images

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