A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images. (January 2017)

• Record Type: Journal Article
• Authors: Liu, Qing; Zou, Beiji; Chen, Jie; Ke, Wei; Yue, Kejuan; Chen, Zailiang; Zhao, Guoying
• Is Part Of: Computerized medical imaging and graphics. Volume 55(2017)
• Page Start: 78
• Page End: 86
• Publication Date: 2017-01
• Subjects: Diabetic retinopathy -- Colour retinal fundus image -- Optic disk segmentation -- Exudate location -- Exudate segmentation; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2016.09.001
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: A location-to-segmentation strategy for exudate segmentation is presented. We propose to use the histogram of CLBP to describe the local texture structures of the exudate regions. The size prior and regional contrast prior about the exudate regions for segmentation are exploited. 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.