Nuclear main component spectrum Hash method for diabetic eyeground image classification

Abstract

The invention discloses a nuclear main component spectrum Hash method for diabetic eyeground image classification. The method comprises the steps of performing preprocessing and segmentation operationon diabetic eyeground image data, converting the processed eyeground image data to a vector form; then extracting nonlinear characteristic information in the eyeground image data in a nuclear main component analysis algorithm; then converting the data to a binary code form, representing the eyeground image sample data by means of the characteristic value and the characteristic function value of an Laplace-Beltrami operator; and finally converting the sample characteristic function value to the binary code by means of a threshold, and performing effective classification of the diabetic eyeground image in a Hamming space by means of a nearest neighbor algorithm. The nuclear main component spectrum Hash method has advantages of sufficiently extracting the complicated nonlinear diabetic eyeground image data characteristic, realizing relatively high classification accuracy and effectively reducing calculation complexity in large-scale eyeground image classification.

Description

[0001] Technical field: [0002] The invention relates to medical image classification and detection, in particular to a kernel principal component spectrum hashing method for diabetic fundus image classification. [0003] Background technique: [0004] Diabetes is a disease with a high incidence rate and has become a major threat to human health. Early detection of diabetes can effectively reduce the cost of treatment, on the contrary, the treatment of advanced diabetes is expensive. Diabetes often leads to retinal abnormalities, a microvascular complication of diabetes known as diabetic retinopathy. Fundus images can be used to monitor retinal abnormalities, so fundus image classification has now become an effective method for non-invasive detection of diabetes. The classification accuracy of fundus image diagnosis is evaluated by using sensitivity and specificity. Sensitivity refers to the percentage of abnormal fundus being correctly classified, and specificity refers to ...

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Problems solved by technology

Early detection of diabetes can effectively reduce the cost of treatment, in contrast to the treatment of advanced diabetes is expensive

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