In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Intere...In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.展开更多
文摘In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD)using soft computing techniques is presented. As the biomedical images such asfundus images are often acquired in high resolution, the Region of Interest (ROI)for glaucoma diagnosis must be selected at first to reduce the complexity of anysystem. The DMGD system uses a series of pre-processing;initial cropping by thegreen channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), bloodvessel detection and removal by Gaussian Derivative Filters (GDF) and inpaintingalgorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequencydomain features from LAWS are generated from the corresponding ROI forfurther classification using kernel based Support Vector Machine (SVM). TheDMGD system performances are validated using four fundus image databases;ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels;LinearKernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel,Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernelbased classifies from the properly segmented ROI.
