Automated pterygium detection method of anterior segment photographed images. (February 2018)

• Record Type: Journal Article
• Authors: Wan Zaki, Wan Mimi Diyana; Mat Daud, Marizuana; Abdani, Siti Raihanah; Hussain, Aini; Mutalib, Haliza Abdul
• Is Part Of: Computer methods and programs in biomedicine. Volume 154(2018)
• Page Start: 71
• Page End: 78
• Publication Date: 2018-02
• Subjects: Ocular disease -- Anterior segment photographed image -- Shape feature -- Pterygium screening system -- Support vector machine; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.10.026
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An automated pterygium detection system using anterior segment photographed images (ASPI) is proposed. ASPIs processed in preprocessing module to enhance pterygium tissue. Pterygium tissue detected by the segmentation of corneal region. Pterygium and nonpterygium case is classified. Abstract: Background and bjective: Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images. Methods: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network. Results: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively. Conclusion: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.