Optic disc localization using interference map and localized segmentation using grab cut

Joshi, Pratik; Ks, Ranjth Raj; Masilamani,; Alike, Jahnavi; Suresh, K. V.; Kumaresh, K.

doi:10.1080/00051144.2020.1807790

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…In the study by Mvoulana et al [23], the OD is localized based on combining a brightness criterion and a template matching method. Finally, the OD boundary was developed by Joshi et al [24] and used the interference map and thresholding technique to localize the OD. Finally, OD occurrence was detected using the graph cut, a method presented by Naqvi et al [25] in OD detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Wisaeng¹

2023

Computer Systems Science and Engineering

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In recent days, detecting Optic Disc (OD) in retinal images has been challenging and very important to the early diagnosis of eye diseases. The process of detecting the OD is challenging due to the diversity of color, intensity, brightness and shape of the OD. Moreover, the color similarities of the neighboring organs of the OD create difficulties during OD detection. In the proposed Fuzzy K-Means Threshold (FKMT) and Morphological Operation with Pixel Density Feature (MOPDF), the input retinal images are coarsely segmented by fuzzy K-means clustering and thresholding, in which the OD is classified from its neighboring organs with intensity similarities. Then, the segmented images are given as the input to morphological operation with pixel density feature calculations, which reduce the false detection in the small pixel of the OD. Finally, the OD area is detected by applying the Sobel edge detection method, which accurately detects the OD from the retinal images. After detection optimization, the proposed method achieved Sensitivity (SEN), Specificity (SPEC) and Accuracy (ACC), with 96.74%, 96.78% and 96.92% in DiaretDB0 (Standard Diabetic Retinopathy Database Calibration level 0), 97.12%, 97.10% and 97.75% in Dia-retDB1 (Standard Diabetic Retinopathy Database Calibration level 1) and 97.19%, 97.47% and 97.43% in STARE (Structured Analysis of the Retina) dataset respectively. The experimental results demonstrated the proposed method's efficiency for segmenting and detecting OD areas.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Wisaeng¹

2023

Computer Systems Science and Engineering

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“…Probability Density Functions (PDF) are used to extract the texture characteristics. The Probability density is computed using GLDM from different images [28] rather than the original image.…”

Section: Gldm Featuresmentioning

confidence: 99%

Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

Sudha¹,

Srinivasan²,

Devi³

2023

Computer Systems Science and Engineering

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The substantial vision loss due to Diabetic Retinopathy (DR) mainly damages the blood vessels of the retina. These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage, if this problem doesn't exhibit initially, that leads to permanent blindness. So, this type of disorder can be only screened and identified through the processing of fundus images. The different stages in DR are Micro aneurysms (Ma), Hemorrhages (HE), and Exudates, and the stages in lesion show the chance of DR. For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image. The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor (KNN) classifier, Support Vector Machine (SVM) classifier, and Cascaded Rotation Forest (CRF) classifier. Over this classifier, the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity, sensitivity, and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%. Among these Cascaded Rotation Forest (CRF) classifier has more accuracy than others.

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Optic disc localization using interference map and localized segmentation using grab cut

Cited by 2 publications

References 10 publications

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Automatic Optic Disc Detection in Retinal Images Using FKMT‒MOPDF

Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

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