Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images.
Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these ima...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 12; pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research Journal Article |
| Publicado: |
Springer Nature
Dec2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140292669&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140292669 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2019 vid: 43 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 140292669 140292669 140292669 10.1007/s10916-019-1452-9 140292669 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic Annotation of Retinal Layers in Optical Coherence Tomography Images. aug: au: Dodo, Bashir Isa Li, Yongmin Eltayef, Khalid Liu, Xiaohui affil: Department of Computer Science, Brunel University London, Kingston Lane, UB83PH, Uxbridge, UK sug: subj: Image Processing, Computer Assisted Image Enhancement Tomography, Optical Coherence Retinal Diseases Diagnosis Human Nerve Fibers Pathology ab: Early diagnosis of retinal OCT images has been shown to curtail blindness and visual impairments. However, the advancement of ophthalmic imaging technologies produces an ever-growing scale of retina images, both in volume and variety, which overwhelms the ophthalmologist ability to segment these images. While many automated methods exist, speckle noise and intensity inhomogeneity negatively impacts the performance of these methods. We present a comprehensive and fully automatic method for annotation of retinal layers in OCT images comprising of fuzzy histogram hyperbolisation (FHH) and graph cut methods to segment 7 retinal layers across 8 boundaries. The FHH handles speckle noise and inhomogeneity in the preprocessing step. Then the normalised vertical image gradient, and it's inverse to represent image intensity in calculating two adjacency matrices and then the FHH reassigns the edge-weights to make edges along retinal boundaries have a low cost, and graph cut method identifies the shortest-paths (layer boundaries). The method is evaluated on 150 B-Scan images, 50 each from the temporal, foveal and nasal regions were used in our study. Promising experimental results have been achieved with high tolerance and adaptability to contour variance and pathological inconsistency of the retinal layers in all (temporal, foveal and nasal) regions. The method also achieves high accuracy, sensitivity, and Dice score of 0.98360, 0.9692 and 0.9712, respectively in segmenting the retinal nerve fibre layer. The annotation can facilitate eye examination by providing accurate results. The integration of the vertical gradients into the graph cut framework, which captures the unique characteristics of retinal structures, is particularly useful in finding the actual minimum paths across multiple retinal layer boundaries. Prior knowledge plays an integral role in image segmentation. pubtype: Academic Journal doctype: diagnostic images equations & formulas research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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