Comput. Biol. Med. - Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint.

Tópicos

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Resumo

Automatic segmentation of retinal layers in spectral domain optical coherence tomography (SD-OCT) images plays a vital role in the quantitative assessment of retinal disease, because it provides detailed information which is hard to process manually. A number of algorithms to automatically segment retinal layers have been developed; however, accurate edge detection is challenging. We developed an automatic algorithm for segmenting retinal layers based on dual-gradient and spatial correlation smoothness constraint. The proposed algorithm utilizes a customized edge flow to produce the edge map and a convolution operator to obtain local gradient map in the axial direction. A valid search region is then defined to identify layer boundaries. Finally, a spatial correlation smoothness constraint is applied to remove anomalous points at the layer boundaries. Our approach was tested on two datasets including 10 cubes from 10 healthy eyes and 15 cubes from 6 patients with age-related macular degeneration. A quantitative evaluation of our method was performed on more than 600 images from cubes obtained in five healthy eyes. Experimental results demonstrated that the proposed method can estimate six layer boundaries accurately. Mean absolute boundary positioning differences and mean absolute thickness differences (mean?SD) were 4.43?3.32 ?m and 0.22?0.24 ?m, respectively.

Resumo Limpo

automat segment retin layer spectral domain optic coher tomographi sdoct imag play vital role quantit assess retin diseas provid detail inform hard process manual number algorithm automat segment retin layer develop howev accur edg detect challeng develop automat algorithm segment retin layer base dualgradi spatial correl smooth constraint propos algorithm util custom edg flow produc edg map convolut oper obtain local gradient map axial direct valid search region defin identifi layer boundari final spatial correl smooth constraint appli remov anomal point layer boundari approach test two dataset includ cube healthi eye cube patient agerel macular degener quantit evalu method perform imag cube obtain five healthi eye experiment result demonstr propos method can estim six layer boundari accur mean absolut boundari posit differ mean absolut thick differ meansd m m respect

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