Higher level brain glioma survival period prediction method based on sparse representation framework and higher level brain glioma survival period prediction system based on sparse representation framework

Abstract

The invention belongs to the technical field of medical image processing, and particularly provides a higher level brain glioma survival period prediction method based on a sparse representation framework and a higher level brain glioma survival period prediction system based on the sparse representation framework. Firstly the tumor area in an MRI image is segmented by using an image segmentationmethod based on a convolutional neural network; then the global features of the tumor area are extracted by using the method based on local SIFT feature sparse representation; feature selection is performed by using the model of combining sparse representation and structure preserving for reducing the feature redundancy and enhancing the survival period prediction accuracy, and a few high-stability and high-resolution features are selected to predict the survival period; and finally whether the survival period of the patient is greater than 22 months is predicted by using a multi-feature collaboration sparse representation classification method with combination of the multimodal features. The method has high survival period prediction accuracy, and the whole process is automatically completed by the computer without manual intervention so that the method can be used for postoperative survival period prediction of the clinical higher level brain glioma.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a multimodal MRI image processing method and system, in particular to a method and system for predicting the survival period of higher-level brain gliomas. Background technique [0002] The World Health Organization (WHO) defines higher-grade gliomas as III and IV grades not only have poor prognosis clinically, but also have a large difference in overall survival between different patients. Accurate postoperative survival prediction can provide optimal guidance for tumor treatment plan, which has important clinical research value. WHO pathological grade, image features and some basic clinical information including age and sex are widely used in survival prediction research. However, most of these methods use manual features or engineering features guided by previous clinical experience for research and analysis, which limits the comprehensive utiliza...

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Problems solved by technology

However, most of these methods use manual features or engineering features guided by previous clinical experience for research and analysis, which limits the comprehensive utilization of deep pathological features contained in biomedical images.

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