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dc.contributor.authorAl-Fahdawi, Shumoos*
dc.contributor.authorQahwaji, Rami S.R.*
dc.contributor.authorAl-Waisy, Alaa S.*
dc.contributor.authorIpson, Stanley S.*
dc.contributor.authorMalik, R.A.*
dc.contributor.authorBrahma, A.*
dc.contributor.authorChen, X.*
dc.date.accessioned2016-08-11T11:33:09Z
dc.date.available2016-08-11T11:33:09Z
dc.date.issued2016-10
dc.identifier.citationAl-Fahdawi S, Qahwaji R, Al-Waisy AS et al. (2016) A fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal images. Computer Methods and Programs in Biomedicine. 135: 151-166.en_US
dc.identifier.urihttp://hdl.handle.net/10454/8773
dc.descriptionYesen_US
dc.description.abstractDiabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the nerve structures can assist the early diagnosis of this disease. This paper proposes a robust, fast and fully automatic nerve segmentation and morphometric parameter quantification system for corneal confocal microscope images. The segmentation part consists of three main steps. First, a preprocessing step is applied to enhance the visibility of the nerves and remove noise using anisotropic diffusion filtering, specifically a Coherence filter followed by Gaussian filtering. Second, morphological operations are applied to remove unwanted objects in the input image such as epithelial cells and small nerve segments. Finally, an edge detection step is applied to detect all the nerves in the input image. In this step, an efficient algorithm for connecting discontinuous nerves is proposed. In the morphometric parameters quantification part, a number of features are extracted, including thickness, tortuosity and length of nerve, which may be used for the early diagnosis of diabetic polyneuropathy and when planning Laser-Assisted in situ Keratomileusis (LASIK) or Photorefractive keratectomy (PRK). The performance of the proposed segmentation system is evaluated against manually traced ground-truth images based on a database consisting of 498 corneal sub-basal nerve images (238 are normal and 260 are abnormal). In addition, the robustness and efficiency of the proposed system in extracting morphometric features with clinical utility was evaluated in 919 images taken from healthy subjects and diabetic patients with and without neuropathy. We demonstrate rapid (13 seconds/image), robust and effective automated corneal nerve quantification. The proposed system will be deployed as a useful clinical tool to support the expertise of ophthalmologists and save the clinician time in a busy clinical setting.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1016/j.cmpb.2016.07.032en_US
dc.rights© 2016 Elsevier B. V. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectDiabetes; Diabetic peripheral neuropathy; Corneal confocal microscopy; Corneal subbasal epithelium; Automatic nerve segmentation; Anisotropic diffusion filteringen_US
dc.titleA fully automatic nerve segmentation and morphometric parameter quantification system for early diagnosis of diabetic neuropathy in corneal imagesen_US
dc.status.refereedYesen_US
dc.date.Accepted2016-07-22
dc.date.application2016-07-27
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-25T15:25:06Z


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