Image computer-aided diagnosis method for multi-sequence nuclear magnetic resonance images

Abstract

The invention discloses an image computer-aided diagnosis method for multi-sequence nuclear magnetic resonance images, belongs to the field of computer-aided diagnosis, and relates to a computer-aided diagnosis method for multi-sequence image processing, texture feature extraction, classification and decision fusion of magnetic resonance imaging (MRI)-based T1WI, T2WI, an arterial phase, a portal vein phase and an equilibrium phase. According to the method, five sequences of the T1WI, the T2WI, the arterial phase, the portal vein phase and the equilibrium phase of MRI are integrated under a digital image processing and mode identification framework, and the image computer-aided diagnosis is completed by means of a neural network, a voting mechanism and a decision-making tree according to three levels of region of interest (ROI) processing, multi-sequence MRI classification and individual classification. By the method, multi-parameter, multi-sequence and multidirectional imaging is provided, and a combined classifier can select a sequence having the optimal distinguishing performance from the five sequences according to different stages of an anomaly structure to serve as the classification attribute of the corresponding stage. The image computer-aided diagnosis method has the advantages of rich information, clear levels and high classification accuracy.

Description

technical field [0001] The invention belongs to the field of computer-aided judgment based on nuclear magnetic resonance images, and relates to a computer-aided judgment method based on MRI-based T1WI, T2WI, arterial phase, portal vein phase, and equilibrium phase multi-sequence image processing, texture feature extraction, classification and decision fusion . Background technique [0002] At present, the image computer-aided judgment method technology based on magnetic resonance imaging (MRI) is still in the initial stage of development, the accuracy of judgment needs to be improved, and there are many deficiencies. (1) Most of the methods are based on the classification of X-ray computerized tomography (CT) images, and the research on MRI is relatively less involved. (2) In MRI-based methods, most algorithms only use a single image sequence, but do not make full use of the advantage of MRI having multiple sequences. For example, Zeng, Y.F. et al. published the article "C...

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(2) In MRI-based methods, most algorithms only use a single image sequence, but do not make full use of the advantage of MRI having multiple sequences

For example, Zeng, Y.F. et al. published the article "Computer-Aided Diagnosis Based on MRI of Liver Fiber Texture Features" published in Advanced Materials Research in 2013, only using MR dynamic contrast vein sequence classifica

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