A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
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.
School of Information Science and Engineering, Central South University, Changsha 410083, China; Ministry of Education-China Mobile Joint Laboratory for Mobile Health, Changsha 410083, China; Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90570, Finland. Electronic address: qing.liu.411@gmail.com.
Qing
Q
Liu
School of Information Science and Engineering, Central South University, Changsha 410083, China; Ministry of Education-China Mobile Joint Laboratory for Mobile Health, Changsha 410083, China. Electronic address: bjzou@csu.edu.cn.
Beiji
B
Zou
Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90570, Finland. Electronic address: jiechen@ee.oulu.fi.
Jie
J
Chen
Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90570, Finland. Electronic address: wke@ee.oulu.fi.
Wei
W
Ke
Hunan First Normal University, School of Information Science and Engineering, Changsha 410205, China. Electronic address: 11994602@qq.com.
Kejuan
K
Yue
School of Information Science and Engineering, Central South University, Changsha 410083, China; Ministry of Education-China Mobile Joint Laboratory for Mobile Health, Changsha 410083, China. Electronic address: zailiangchencs@gmail.com.
Zailiang
Z
Chen
Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90570, Finland. Electronic address: gyzhao@ee.oulu.fi.
Guoying
G
Zhao
D000465:Algorithms; D003116:Color; D003930:Diabetic Retinopathy / Q000000981:diagnostic imaging*; D005122:Exudates and Transudates / Q000000981:diagnostic imaging*; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009898:Optic Disk / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D012171:Retinal Vessels / Q000000981:diagnostic imaging*
Colour retinal fundus image; Diabetic retinopathy; Exudate location; Exudate segmentation; Optic disk segmentation