Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index. (1st May 2017)

• Record Type: Journal Article
• Authors: Acharya, U. Rajendra; Mookiah, Muthu Rama Krishnan; Koh, Joel E.W.; Tan, Jen Hong; Bhandary, Sulatha V.; Rao, A. Krishna; Hagiwara, Yuki; Chua, Chua Kuang; Laude, Augustinus
• Is Part Of: Computers in biology and medicine. Volume 84(2017)
• Page Start: 59
• Page End: 68
• Publication Date: 2017-05-01
• Subjects: Decision support system -- Diabetic macular edema -- Discrete cosine transform -- Discrete wavelet transform -- Fundus imaging -- Locality sensitive discriminant analysis -- Radon transform; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.03.016
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME. Graphical abstract: Highlights: Grading of diabetic macular edema using fundus images. Radon transform, DWT and DCT methods are employed. Classification accuracy is 100% (private) and 97% (MESSIDOR). Developed a maculopathy index.