The invention discloses a partitioning
algorithm for
choroidal neovascularization in an OCT image. The
algorithm includes steps of S01, designing a structure prior learning method for a training image and constructing a structure prior matrix used for distinguishing a
choroidal neovascularization zone and a background zone; S02, converting the OCT original image to a saliency enhancing image used for enhancing the saliency of the
choroidal neovascularization zone based on the structure prior matrix; S03, adopting multi-scale analysis on the saliency enhancing image and dividing the saliency enhancing image into m scales; S04, acquiring m trained convolutionneural
network model based on each scale training; S05,
processing a testing image by utilizing the step S01, S02, S03 and performing testing by utilizing the trained convolutionneural
network model in the step S04, outputting m portioning results and fusing the m partitioning results into the final partitioning result. By adopting the
algorithm provided by the invention, the precision of partitioning of the choroidal
neovascularization in the OCT image can be improved distinctively.