Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

Wisaeng, Kittipol; Sa-ngiamvibool, Worawat

doi:10.1109/access.2018.2890426

Cited by 46 publications

(12 citation statements)

References 49 publications

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“…For the purpose of improving the exactness without utilizing the classifiers, another strategy for the exudate division is suggested by kittipol wisaeng et al [45]. The morphological closing operator is utilized to circumscribe the OD and binary dilation is utilized to recreate the image with a superior OD identified image.…”

Section: Detection Of Exudatesmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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In this paper existing writing for computer added diagnosis (CAD) based identification of lesions that might be connected in the early finding of Diabetic Retinopathy (DR) is talked about. The recognition of sores, for example, Microaneurysms (MA), Hemorrhages (HEM) and Exudates (EX) are incorporated in this paper. A range of methodologies starting from conventional morphology to deep learning techniques have been discussed. The different strategies like hand crafted feature extraction to automated CNN based component extraction, single lesion identification to multi sore recognition have been explored. The different stages in each methods beginning from the image preprocessing to classification stage are investigated. The exhibition of the proposed strategies are outlined by various performance measurement parameters and their used data sets are tabulated. Toward the end we examined the future headings.

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Section: Detection Of Exudatesmentioning

confidence: 99%

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Jebran P*,

Gupta

2020

IJRTE

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“…Morphological closing is used to eliminate the small-size pixels on the result of the thresholding process. In determining Figure. 3 Partition of the histogram region the size and shape of the structural element, we refer to the previous research conducted by Wisaeng (2019) that used disc structural element with size 7 because it is computationally efficient [19]. The result of this process is then saved for the next process in selecting the overlapping area candidates.…”

Section: Automatic Marking Of Coordinate Pointsmentioning

confidence: 99%

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

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Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

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“…A morphology mean shift algorithm based approach for the detection of exudate lesions in color retinal images was introduced by Wisaeng et.al. [5]. The Mathematical Morphology Algorithm (MMA) is the key element in this method.…”

Section: Introductionmentioning

confidence: 99%

Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

Pradeep¹,

Joseph²

2020

IJECE

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Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%. These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR.

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Exudates Detection Using Morphology Mean Shift Algorithm in Retinal Images

Cited by 46 publications

References 49 publications

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

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