Fundus image-oriented microaneurysm detection method

Abstract

The invention relates to a medical image processing technology, and provides a fundus image-oriented microaneurysm detection method for overcoming the defects of detection of a microaneurysm which isa micro target in the prior art, so as to effectively detect the microaneurysm in a fundus image, and better assist a doctor in diagnosis while realizing automatic detection. The method comprises: preprocessing the fundus image, so that the features of a small target are enhanced, and making and training a data set on a built basic feature extraction network; during detection, extracting image basic features from the input image, and performing blood vessel segmentation on the input image by utilizing a segmentation model to obtain a feature map and a segmentation map for subsequent processing; then integrating an attention mechanism into a feature fusion process to obtain a fused convolution feature layer; inputting the fused convolution feature layer into a candidate region generation network, and obtaining a candidate region by considering the position relationship between the target and the blood vessel; and finally, further classifying and regressing the candidate regions to obtain a detection result.

Description

technical field [0001] The invention relates to a medical image processing technology, in particular to a microaneurysm detection method for fundus images. Background technique [0002] Diabetic retinopathy (Diabetic Retinopathy, DR) is one of the most serious complications of diabetes, and it is also the main cause of blindness in the world. Microaneurysms (MAs) are the earliest detectable tiny lesions in the early stage of DR lesions, and appear as small dark red dots in retinal fundus images. The traditional screening of DR lesions mainly relies on the diagnosis of retinal fundus images by ophthalmologists, which not only requires high professional skills of doctors, but also the diagnosis process is time-consuming and labor-intensive. As the incidence of diabetes continues to rise, medical resources are increasingly scarce. Therefore, automatic detection of microaneurysms is of great significance for DR screening and assisting doctors in diagnosis. [0003] In the pri...

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Problems solved by technology

[0006] There are still deficiencies in the above-mentioned feature pyramid schemes. When performing feature fusion, the importance of different layers of features is not considered, and most target detection schemes mainly focus on the features of the target itself, thus ignoring the correlation between the target and the surrounding environment. The convolutional neural network is regarded as a black box, which does not take into account the lesion information closely related to the diagnosis

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