Automatic Detection of Exudates in Digital Color Fundus Images Using Superpixel Multi-Feature Classification

Zhou, Wei; Wu, Chengdong; Yi, Yugen; Du, Wenyou

doi:10.1109/access.2017.2740239

Cited by 66 publications

(46 citation statements)

References 27 publications

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“…For exudate classification Wei Zhou et al [42] proposed a series of super pixel exudate candidates which is a simple linear iterative clustering. 19 Independent of the varieties of intensities, brightness and faint edges in the retinal image, Jaskirat Kaur et al [43] proposed a vigorous exudate division technique dependent on dynamic decision thresholding.…”

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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“…Hence many researchers have been focused over developing efficient methodologies to achieve an improved precision of OD boundary extraction. Based on the methodology accomplished at extraction, they are classified as Classifier based methods [6][7][8][9][10], Template matching based methods [11][12][13][14][15], Morphology based methods [16][17][18] and Active Contour Model based methods. Under the first class, i.e., Classifier based methods; a number of methods are developed.…”

Section: Literature Surveymentioning

confidence: 99%

“…Further, Zhou et. al., [10] developed a novel approach called as Super-pixel multi-feature classification for automatic detection of Exudates which have similar characteristics with OD at the brightness level. Totally, 20 features, including 19 multi-channel intensity features and a novel contextual feature, are proposed for characterizing each candidate.…”

Section: Literature Surveymentioning

confidence: 99%

“…The retinal vessels can be eliminated from the image I by detecting, for every pixel, the maximum gray-level variance and considering the rotation at which that was obtained. Mathematically it is formulated as, (10) Where is a structuring element at rotation r and is a non-vessel sub image. Here the term gives a maximum gray-level value at rotation r in the neighborhood of defined by the structuring element.…”

Section: Blood Vessel Removalmentioning

confidence: 99%

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Optic Disk Segmentation through Edge Density Filter in Retinal Images

Reddy*,

Ravindran

2020

IJITEE

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Accurate and Efficient Detection of Optic Disk (OD) is an important task for the diagnosis of various eye related diseases such as Diabetic Retinopathy, Glaucoma, and papilledema in automatic retinal image analysis. This paper presents a novel and robust OD detection framework which starts with the detection of OD pixel, a center of OD region. Next, based on the ODP, a sub-image extraction is accomplished and it is processed for blood vessel removal through morphological technique. Finally, a new filter, called as Edge Density Filter (EDF) is accomplished to find out the extract the OD region in the Blood vessel removed, Edge detected and binarized sub-image. Two publicly available datasets, MESSIDOR and DRIVE were used to evaluate the performance of proposed method and the performance is measured through performance metrics such as Overlap Score, Sensitivity and Accuracy.

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“…To transfer the latter to our case, we may investigate whether connecting tiles of similar boundary likelihood can omit the need for an initial MCG image segmentation: By using Fully Convolutional Networks (FCNs) [52] each pixel of the input image would be assigned a boundary likelihood, which can be connected using Ultrametric Contour Maps (UCMs) [53] included in MCG [54]. Connecting pixels of corresponding boundary likelihoods could also be realized by using MCG-based contour closure [55], line integral convolution [56], or template matching [57].…”

Section: Comparison To Previous Studiesmentioning

confidence: 99%

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

et al. 2019

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Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02-0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

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Automatic Detection of Exudates in Digital Color Fundus Images Using Superpixel Multi-Feature Classification

Cited by 66 publications

References 27 publications

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Detection of Lesions for Diabetic Retinopathy By using Machine Learning Algorithms

Optic Disk Segmentation through Edge Density Filter in Retinal Images

Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

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