Retinal Layer Segmentation in Optical Coherence Tomography Images
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...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8871107/ |
| _version_ | 1818347922634309632 |
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| author | Bashir Isa Dodo Yongmin Li Djibril Kaba Xiaohui Liu |
| author_facet | Bashir Isa Dodo Yongmin Li Djibril Kaba Xiaohui Liu |
| author_sort | Bashir Isa Dodo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T17:41:52Z |
| format | Article |
| id | doaj.art-6d1c67416a2d46e495605344ac5579bb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T17:41:52Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6d1c67416a2d46e495605344ac5579bb2022-12-21T23:36:44ZengIEEEIEEE Access2169-35362019-01-01715238815239810.1109/ACCESS.2019.29477618871107Retinal Layer Segmentation in Optical Coherence Tomography ImagesBashir Isa Dodo0https://orcid.org/0000-0001-5467-4324Yongmin Li1Djibril Kaba2Xiaohui Liu3Department of Computer Science, Brunel University London, Uxbridge, U.K.Department of Computer Science, Brunel University London, Uxbridge, U.K.Connected Places Catapult, The Alan Turing Institute, London, U.K.Department of Computer Science, Brunel University London, Uxbridge, U.K.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.https://ieeexplore.ieee.org/document/8871107/Medical image analysisoptical coherence tomographyfuzzy image processinggraph-cutcontinuous max-flow |
| spellingShingle | Bashir Isa Dodo Yongmin Li Djibril Kaba Xiaohui Liu Retinal Layer Segmentation in Optical Coherence Tomography Images IEEE Access Medical image analysis optical coherence tomography fuzzy image processing graph-cut continuous max-flow |
| title | Retinal Layer Segmentation in Optical Coherence Tomography Images |
| title_full | Retinal Layer Segmentation in Optical Coherence Tomography Images |
| title_fullStr | Retinal Layer Segmentation in Optical Coherence Tomography Images |
| title_full_unstemmed | Retinal Layer Segmentation in Optical Coherence Tomography Images |
| title_short | Retinal Layer Segmentation in Optical Coherence Tomography Images |
| title_sort | retinal layer segmentation in optical coherence tomography images |
| topic | Medical image analysis optical coherence tomography fuzzy image processing graph-cut continuous max-flow |
| url | https://ieeexplore.ieee.org/document/8871107/ |
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