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We propose a novel retinal layer segmentation method to accurately segment 10 retinal layers in optical coherence tomography (OCT) images with intraretinal fluid. The method used a fan filter to enhance the linear information pertaining to retinal boundaries in an OCT image by reducing the effect of vessel shadows and fluid regions. A random forest classifier was employed to predict the location of the boundaries. Two novel methods of boundary redirection (SR) and similarity correction (SC) were combined to carry out boundary tracking and thereby accurately locate retinal layer boundaries. Experiments were performed on healthy controls and subjects with diabetic macular edema (DME). The proposed method required an average of 415 s for healthy controls and of 482 s for subjects with DME and achieved high accuracy for both groups of subjects. The proposed method requires a shorter running time than previous methods and also provides high accuracy. Thus, the proposed method may be a better choice for small training datasets. [ABSTRACT FROM AUTHOR]

We propose a novel retinal layer segmentation method to accurately segment 10 retinal layers in optical coherence tomography (OCT) images with intraretinal fluid. The method used a fan filter to enhance the linear information pertaining to retinal boundaries in an OCT image by reducing the effect of vessel shadows and fluid regions. A random forest classifier was employed to predict the location of the boundaries. Two novel methods of boundary redirection (SR) and similarity correction (SC) were combined to carry out boundary tracking and thereby accurately locate retinal layer boundaries. Experiments were performed on healthy controls and subjects with diabetic macular edema (DME). The proposed method required an average of 415[Formula: see text]s for healthy controls and of 482[Formula: see text]s for subjects with DME and achieved high accuracy for both groups of subjects. The proposed method requires a shorter running time than previous methods and also provides high accuracy. Thus, the proposed method may be a better choice for small training datasets.