Classification model construction method and device used for macula degeneration region segmentation

Abstract

The invention discloses a classification model construction method used for macula degeneration region segmentation. The method includes the following steps: selecting multiple fundus images, conducting graying processing on the fundus images to obtain multiple gray scale images, and sampling foregrounds and backgrounds of the gray scale images to obtain samples; adopting a generalized low-rank approximate method to obtain a transformation matrix, conducting dimension reduction on the samples on the basis of the transformation matrix, and obtaining a low-rank approximate matrix of the samples; adding label information into the low-rank approximate matrix of the samples to perform a supervision function, and constructing manifold regularization items; establishing a target function through the generalized low-rank approximate method and the manifold regularization items, solving the target function through an iterative optimization method, and obtaining an optimal transformation matrix and an optimal low-rank approximate matrix of the samples; and constructing a classification model on the basis of the optimal low-rank approximate matrix and the label information. The classification model can extract low dimensional and also highly distinguishable feature descriptors, and can improve the segmentation precision.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a classification model construction method, equipment and image segmentation method for segmenting macular lesion regions of fundus images. Background technique [0002] Eyes are the most important organ for human beings to obtain information. The macula is located at the back of the eyeball and is an important tissue for people to perceive external light and objects. Lesions in this area can cause vision loss or even blindness, which is one of the important causes of blindness in the elderly. When doctors diagnose drusen in fundus images, there are shortcomings such as low accuracy, poor repeatability, and many subjective factors. Therefore, there is an urgent need for the application and research of macular degeneration region segmentation technology to meet the clinical needs of macular degeneration screening, diagnosis, treatment and other auxiliary medical care. ...

AI Technical Summary

Problems solved by technology

[0005] However, there are still relatively few researches on the application of supervised feature learning methods in fundus image segmentation.

However, other hand-designed features for fundus image segmentation do not have strong distinguishing and descriptive capabilities, and cannot obtain more accurate segmentation results.

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