PMID- 27586488
OWN - NLM
STAT- MEDLINE
DCOM- 20170313
LR  - 20170313
IS  - 1872-7565 (Electronic)
IS  - 0169-2607 (Linking)
VI  - 135
DP  - 2016 Oct
TI  - A fully automatic nerve segmentation and morphometric parameter quantification 
      system for early diagnosis of diabetic neuropathy in corneal images.
PG  - 151-66
LID - S0169-2607(16)30175-4 [pii]
LID - 10.1016/j.cmpb.2016.07.032 [doi]
AB  - Diabetic 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.
CI  - Copyright (c) 2016 Elsevier Ireland Ltd. All rights reserved.
FAU - Al-Fahdawi, Shumoos
AU  - Al-Fahdawi S
AD  - School of Electrical Engineering and Computer Science, University of Bradford, 
      Bradford, UK. Electronic address: sun_algold@yahoo.com.
FAU - Qahwaji, Rami
AU  - Qahwaji R
AD  - School of Electrical Engineering and Computer Science, University of Bradford, 
      Bradford, UK.
FAU - Al-Waisy, Alaa S
AU  - Al-Waisy AS
AD  - School of Electrical Engineering and Computer Science, University of Bradford, 
      Bradford, UK.
FAU - Ipson, Stanley
AU  - Ipson S
AD  - School of Electrical Engineering and Computer Science, University of Bradford, 
      Bradford, UK.
FAU - Malik, Rayaz A
AU  - Malik RA
AD  - Division of Medicine, Weill Cornell Medical College in Qatar, Doha, Qatar; Centre 
      for Endocrinology and Diabetes, Institute of Human Development, University of 
      Manchester and the Manchester Royal Infirmary, Central Manchester Hospital 
      Foundation Trust, Manchester, UK.
FAU - Brahma, Arun
AU  - Brahma A
AD  - Manchester Royal Eye Hospital, Central Manchester University Hospitals NHS 
      Foundation Trust, Manchester Academic Health Science Centre, Manchester, M13 9WL, 
      UK.
FAU - Chen, Xin
AU  - Chen X
AD  - Centre for Imaging Sciences, Institute of Population Health, University of 
      Manchester, Manchester, UK.
LA  - eng
PT  - Journal Article
DEP - 20160727
PL  - Ireland
TA  - Comput Methods Programs Biomed
JT  - Computer methods and programs in biomedicine
JID - 8506513
SB  - IM
MH  - Autonomic Nervous System/*anatomy & histology
MH  - Case-Control Studies
MH  - Cornea/*innervation
MH  - Diabetic Nephropathies/diagnosis/*pathology
MH  - Humans
OTO - NOTNLM
OT  - Anisotropic diffusion filtering
OT  - Automatic nerve segmentation
OT  - Corneal confocal microscopy
OT  - Corneal subbasal epithelium
OT  - Diabetes
OT  - Diabetic peripheral neuropathy
EDAT- 2016/09/03 06:00
MHDA- 2017/03/14 06:00
CRDT- 2016/09/03 06:00
PHST- 2016/02/26 00:00 [received]
PHST- 2016/06/09 00:00 [revised]
PHST- 2016/07/22 00:00 [accepted]
PHST- 2016/09/03 06:00 [entrez]
PHST- 2016/09/03 06:00 [pubmed]
PHST- 2017/03/14 06:00 [medline]
AID - S0169-2607(16)30175-4 [pii]
AID - 10.1016/j.cmpb.2016.07.032 [doi]
PST - ppublish
SO  - Comput Methods Programs Biomed. 2016 Oct;135:151-66. doi: 
      10.1016/j.cmpb.2016.07.032. Epub 2016 Jul 27.