University of Oulu

Qing Liu, Beiji Zou, Jie Chen, Wei Ke, Kejuan Yue, Zailiang Chen, Guoying Zhao, A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images, Computerized Medical Imaging and Graphics, Volume 55, 2017, Pages 78-86, ISSN 0895-6111, https://doi.org/10.1016/j.compmedimag.2016.09.001

A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images

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Author: Liu, Qing1,2,3; Zou, Beiji1,2; Chen, Jie3;
Organizations: 1School of information science and engineering, Central South university, Changsha 410083, China
2Ministry of Education-China Mobile Joint Laboratory For Mobile Health, Changsha 410083, China
3Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, 90570, Finland
4Hunan First Normal University, School of Information Science and Engineering, Changsha 410205, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2019040811337
Language: English
Published: Elsevier, 2017
Publish Date: 2019-04-08
Description:

Abstract

The automatic exudate segmentation in colour retinal fundus images is an important task in computer aided diagnosis and screening systems for diabetic retinopathy. In this paper, we present a location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images, which includes three stages: anatomic structure removal, exudate location and exudate segmentation. In anatomic structure removal stage, matched filters based main vessels segmentation method and a saliency based optic disk segmentation method are proposed. The main vessel and optic disk are then removed to eliminate the adverse affects that they bring to the second stage. In the location stage, we learn a random forest classifier to classify patches into two classes: exudate patches and exudate-free patches, in which the histograms of completed local binary patterns are extracted to describe the texture structures of the patches. Finally, the local variance, the size prior about the exudate regions and the local contrast prior are used to segment the exudate regions out from patches which are classified as exudate patches in the location stage. We evaluate our method both at exudate-level and image-level. For exudate-level evaluation, we test our method on e-ophtha EX dataset, which provides pixel level annotation from the specialists. The experimental results show that our method achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly. For image-level evaluation, we test our method on DiaRetDB1, and achieve competitive performance compared to the state of the art methods.

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Series: Computerized medical imaging and graphics
ISSN: 0895-6111
ISSN-E: 1879-0771
ISSN-L: 0895-6111
Volume: 55
Pages: 78 - 86
DOI: 10.1016/j.compmedimag.2016.09.001
OADOI: https://oadoi.org/10.1016/j.compmedimag.2016.09.001
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Funding: Q. Liu andW. Ke were partially supported by the scholarship from China Scholarship Council (CSC). B. Zou and Z. Chen were partially supported by the National Natural Science Foundation of China under Grant No.61573380 and No. 61440055. G. Zhao and J. Chen were partially supported by Academy of Finland, Tekes Fidipro Program and Infotech Oulu. K. Yue was partially supported by Scientific Research Fund of Hunan Provincial Education Department under Grant No.13C143.
Copyright information: © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/