Computerized Detection of Macular Edema Using OCT Images Based on Fractal Texture Analysis

Athira, S.C.; Roy, Reena M.; Aneesh, R P

doi:10.1109/cetic4.2018.8530952

Cited by 6 publications

(8 citation statements)

References 8 publications

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“…Authors Year Preprocessing Segmentation Classification Nasrulloh et al [11] 2018 Yes Yes No Keller et al [26] 2016 Yes Yes No Miri et al [96] 2016 Yes Yes No Zhang et al [5] 2015 Yes Yes No Xu et al [27] 2013 Yes Yes No Liu et al [19] 2011 Yes Yes Yes Duan et al [43] 2017 Yes Yes No Sui et al [28] 2017 [106] 2017 Yes Yes No Athira et al [107] 2018 Yes Yes No Gopinath et al [108] 2017 No Yes No Dodo et al [109] 2019 Yes Yes No Duan et al [110] 2015 Yes Yes No Lang et al [111] 2017 Yes Yes No Niu et al [112] 2014 Yes Yes No Rossant et al [113] 2015 Yes Yes No Tian et al [114] 2015 Yes Yes No Huang et al [80] 2019 No Yes Yes Nath et al [82] 2018 Yes Yes Yes Hassan and Hassan [81] 2019 Yes Yes Yes Hassan et al [1] 2016 Yes Yes Yes Fang et al [115] 2017 Yes Yes Yes the B-scans [93,94]. The OCTID was the only publicly available database found with only cases of macular holes pathology [95].…”

Section: Acquisition Of Datamentioning

confidence: 99%

“…The review indicates many types of preprocessing techniques: dilation and erosion [5], median filter [27,107,111], gaussian filter [11,100,101], wiener filter [11,81,99], binary image [26,100], gradient image [26,114], anisotropic difusion filter [5,97,99], image aligment [19,98,103], attenuation coefficient [101], enhanced contrast [1,105], image flattenig [106,114], resize the image [1,107], edge flow [112], sparse filter [112], normalization [81], green channel [81], greyscale [1], morphological operations [1] and others. In Figure 2 is shown the improvements of preprocessing applied to OCT image.…”

Section: Preprocessingmentioning

confidence: 99%

“…Some segmentation techniques applied to OCT images are: snakes or active contour, it is a method used to circumvent objects in an image [105]; graph search, this technique starts from a starting point to an ending point, repeating the process, trying to find the best path [5,26,27]; Dijkstra shortest path search, it is an algorithm that seeks to find the shortest path between two nodes [26,99,107]; local gaussian distribution fitting (LGDF), local entropy defines gray level weights [11]; curvature-based surface cutting, it can be flattened into the plane with low metric distortion [11]; ReLayNet, it is an architecture used to segment retinal layers [80,117]; speed-up robust features (SURF), it is a method used in tasks like object recognition, classification etc [115]; adjusted mean arc length (AMAL), it enables to pass the load limit and turning points, and consequently to follow the post-critical equilibrium trajectories [26]; gradient vector flow (GVF), it is an algorithm that locates object edges [96,118,119].…”

Section: Segmentationmentioning

confidence: 99%

“…Other techniques are: multi-scale spatial pyramid (MSSP), it captures the geometry of retina at multiple scales [19]; geodesic distance method (GDM), it can locate pixels in boundaries of layers [43]; convolutional neural network (CNN), it is pooling, which is a non-linear down-sampling [28,108,120]; dynamic programming (DP), it is a method that divide problems and solves each one separately [65,100,102]; canny edge detection, it is an algorithm that detects edges [97,99,104]; markov gibbs random field (MGRF), it allows to derive a global texture description by specifying local properties of textures [98]; loosely coupled level sets (LCLS), it is a technique that uses local intensity variations to segment layers [101]; structure tensor, it utilizes the gradient of a point with neighborhood to get directions of segmentation [102,104]; Randon Forest (RF), that train the data to estimate boundary probabilities [111,121,122]; and OTSU algorithm, it is used to perform automatic image thresholding [102,107,112]. In Figure 3 is shown an example of segmentation approach applied to an OCT image, in which the top boundary of the ILM and RPE layers are highlighted.…”

Section: Segmentationmentioning

confidence: 99%

See 3 more Smart Citations

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Mendes¹,

Lucena²,

Lucena³

et al. 2020

AIS

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Macular holes are a blinding condition that occur due to overuse of the fovea, in which a hole alters the natural retinal structure. Optical Coherence Tomography (OCT) is a way of mapping and shaping retinal sections without physical contact and has become a powerful tool for diagnosing pathologies. This paper deals with a review of automated segmentation of macular holes in OCT images, detailing its varied possibilities. It may be considered something new, no reviews were made about the topic. The purpose of this review is to show the latest trends, through the approaches in preprocessing and segmentation. Recent studies were used to validate the research, 2011 onwards, from the Science Direct, IEEE, PubMed and Google scholar bases. The objectives, methodology, tools, database, advantages, disadvantages, validation metrics and results of the selected material are analyzed and mentioned. Based on this, techniques and their results are compared. From this, future outlook scenarios of automated segmentation of macular holes in OCT images are mentioned.

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Section: Acquisition Of Datamentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

Section: Segmentationmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Mendes¹,

Lucena²,

Lucena³

et al. 2020

AIS

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show abstract

“…Another method is derived by conducting model-based clustering and found 3 clusters were most effective in basic pigment separation [4]. The use of texture analysis has proved to be superior in terms of detection accuracy, adapted a method called fractal image texture analysis based on OCT images to identify Macular Edema [1]. Their algorithm derived 97.5% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Plant Disease Diagnosis and Classification by Computer Vision using Statistical Texture Feature Extraction Technique and K Nearest Neighbor Classification

Gangadharan,

Kumari,

Dhanasekaran

et al. 2019

IJEAT

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Pest attack and infectious diseases has become more common in the field of agriculture in the recent times. It has become a challenging task to identify the infection or the insect that destructs the plant growth and production. Diagnosing the disease or the insect attack on the plants in the early stage will safe guard the plant growth and the production rate. Timely intervention of technology that deals with disease detection and control method can protect the plants from usage of harmful pesticides. The higher dosage of pesticides impacts the health of human as well as other creatures like birds and animals which directly or indirectly consumes the plant or get in touch with the plants in different circumstances. A Computer vision technique which combines the Digital Image processing and Machine Learning methodology has been proposed to provide pest management solution. The disease detection is based on the statistical texture feature analysis and it is classified using K nearest neighbor classifier. Statistical PCA is combined with SIFT method to extract the key points, which eliminates the non-operational key points and SFTA is used to extract the texture. The system has achieved better result in identifying and differentiating the infection and insect attack on multiple plant taxonomy. The implementation has been performed using MATLAB.

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Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

Pavithra

Kumar

Geetha

et al. 2023

Biocybernetics and Biomedical Engineering

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Computerized Detection of Macular Edema Using OCT Images Based on Fractal Texture Analysis

Cited by 6 publications

References 8 publications

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images: A review

Plant Disease Diagnosis and Classification by Computer Vision using Statistical Texture Feature Extraction Technique and K Nearest Neighbor Classification

Computer aided diagnosis of diabetic macular edema in retinal fundus and OCT images: A review

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