Level Set Segmentation of Retinal OCT Images

Dodo, Bashir Isa; Li, Yongmin; Liu, Xiaohui; Dodo, Muhammad Isa

doi:10.5220/0007577600490056

Cited by 9 publications

(6 citation statements)

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“…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%

“…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. 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%

“…Although the methods in [17], [18] have no outliers beyond the first quartile, their accuracy values start from ∼ 76% which is low compared to the starting value of ∼ 84% of the proposed method. The method in [20] has the second-best start of the first quartile. However, it has underperformed in identifying the NFL in few images due to the constraints set by the method (+ from Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The graph cut methods outperform the level set methods. Although the methods in [18] and [20] have improved performance than classic level set methods, they evolve based on forces to a defined optimum.…”

Section: Resultsmentioning

confidence: 99%

“…The method combines a level set algorithm, hysteresis thresholding model and multi-region continuous max-flow algorithm to extract seven intra-retinal layers including NFL, GCL+IPL, INL, OPL, ONL+IS, OS and RPE. Dodo and al, presented in [20] a level set segmentation method of seven intra-retinal layers. The method uses a region of interest and applies a gradient edges algorithm, which then use to initialise curves for the layers.…”

Section: Related Workmentioning

confidence: 99%

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Retinal Layer Segmentation in Optical Coherence Tomography Images

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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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