Identification method of primary central nervous system lymphoma and glioblastoma based on sparse representation system

Abstract

The invention belongs to the technical field of computer auxiliary diagnosis, and specifically relates to an identification method of primary central nervous system lymphoma and glioblastoma based on a sparse representation system. The method includes: segmenting T1 enhanced and T2 weighted MRI image tumor regions by employing an image segmentation method based on a convolutional neural network; then designing a dictionary learning and sparse representation method, and extracting texture characteristics of the tumor regions; selecting some characteristics with high stability and high resolution for tumor identification by employing an iterative sparse representation characteristic selection method in order to reduce the characteristic redundancy and improve the tumor identification efficiency; and finally establishing a combined sparse representation classification model containing two modals of T1 enhanced or T2 weighted based on the thought of eigenstate fusion in order to improve the tumor identification precision. According to the method, high tumor identification precision can be obtained, manual operation for extraction of identification parameters is avoided, the robustness is high, and the method can be applied to clinic identification of primary central nervous system lymphoma and glioblastoma.

Description

technical field [0001] The invention belongs to the technical field of computer-aided diagnosis, in particular to a method for distinguishing primary brain lymphoma and glioblastoma based on a sparse representation system. Background technique [0002] Clinically, there are large differences in the treatment options for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). Accurate identification of the two before treatment can guide clinicians Making a reasonable treatment plan has important clinical value. However, it is very difficult to accurately distinguish primary brain lymphoma from glioblastoma using some traditional modal MRI images such as T1-weighted, T1-enhanced, T2-weighted, and T2FLAIR, because the images of these two tumors in these modalities exhibit many similar properties. Some advanced MRI imaging methods such as diffusion-weighted imaging and dynamic susceptibility-enhanced perfusion imaging have been used to assist in the diagnosis o...

AI Technical Summary

Problems solved by technology

Some advanced MRI imaging methods such as diffusion-weighted imaging and dynamic susceptibility-enhanced perfusion imaging have been used to assist in the diagnosis of these two tumors, but these methods themselves also have some problems in the extraction of identification parameters [1][2]

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