Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods

Dodo, Bashir Isa; Li, Yongmin; Tucker, Allan; Kaba, Djibril; Liu, Xiaohui

doi:10.1109/cbms.2019.00029

Cited by 7 publications

(10 citation statements)

References 25 publications

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“…Consequently, the reason for the high RMSE in RPE region compared to other methods is because the proposed method has no technique of handling the proximity of the layers. Inherently, due to the regional competition in [18], it adapts to the RPE region topology better than the proposed method, although both methods do not employ region limitation or topology constraints. Generally, region limitation based on retinal layer topology aids in handling incompleteness in [17], [20], [42], while FCM aids in handling inhomogeneity within layers by adaptively estimating values specific to each image in [18] and the proposed method.…”

Section: Resultsmentioning

confidence: 98%

“…The proposed method segments the NFL better, with RMSE and DC of 0.0179 and 0.970, which can be attributed to the preprocessing steps in isolating the layers and the segmentation method's ability to handle intensity inconsistency. The error in Chiu et al [42] is due to the inability of standard shortest path algorithms (e.g., [45]) to handle inhomogeneity such as that of the OCT. Also, [18] outperforms [20] in the NFL because in some cases the later converges at a local minimum.…”

Section: Resultsmentioning

confidence: 99%

“…Generally, region limitation based on retinal layer topology aids in handling incompleteness in [17], [20], [42], while FCM aids in handling inhomogeneity within layers by adaptively estimating values specific to each image in [18] and the proposed method. This can be deduced from the NFL layer performance discussed earlier based on the performance matrix in Table 1, and in comparison of [18] and the proposed method to [17], [42] for the level set and graph cut methods, 2.…”

Section: Resultsmentioning

confidence: 99%

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Retinal Layer Segmentation in Optical Coherence Tomography Images

Dodo

Kaba

et al. 2019

IEEE Access

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The four major causes of blindness are age-related diseases, out of which three affects the retina. These diseases, i.e., glaucoma, diabetic retinopathy, and age-related macular degeneration, require life-long treatment and cause irreversible blindness. Conversely, early diagnosis has been shown to curtail or prevent blindness and visual impairments. A critical element of the clinical diagnosis is the analysis of individual retinal layer properties, as the manifestation of the dominant eye diseases has been shown to correlate with structural changes to the retinal layers. Regrettably, manual segmentation is dependent on the ophthalmologist's level of expertise, and currently becoming impractical due to advancement in imaging modalities. Inherently, much research on computer-aided diagnostic methods is conducted to aid in extracting useful layer information from these images, which were inaccessible without these techniques. However, speckle noise and intensity inhomogeneity remain a challenge with a detrimental effect on the performance of automated methods. In this paper, we propose a method comprising of fuzzy image processing techniques and graph-cut methods to robustly segment optical coherence tomography (OCT) into five (5) distinct layers. Notably, the method establishes a specific region of interest to suppress the interference of speckle noise, while Fuzzy C-means is utilized to build data terms for better integration into the continuous max-flow to handle inhomogeneity. The method is evaluated on 225 OCT B-scan images, and promising experimental results were achieved. The method will allow for early diagnosis of major eye diseases by providing the basic, yet critical layer information necessary for an effective eye examination.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Retinal Layer Segmentation in Optical Coherence Tomography Images

Dodo

Kaba

et al. 2019

IEEE Access

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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 algorithm had an accuracy of 97.5%, sensitivity of 98.9% and specificity of 98.05%. Dodo [109] created an algorithm to segment nine layers. Different from the others that used Dijkstra's Shortest Path in segmentation step, in this work it was applied to the preprocessing step.…”

Section: Selected Workmentioning

confidence: 99%

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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