White matter high signal extraction method and system based on lesion diffusion algorithm

Abstract

The invention discloses a white matter high signal extraction method and system based on a lesion diffusion algorithm. The method comprises the following steps: inputting an original image comprising an FLAIR image, a T1 weighted image and a white matter tissue prior probability graph; carrying out preprocessing operation on the input original image; performing abnormal signal product operation on the preprocessed image to obtain a tissue signal intensity distribution confidence map, and performing initialization and white matter high signal segmentation on the obtained tissue signal intensity distribution confidence map to obtain a white matter high signal probability map; and carrying out image binaryzation on the white matter high-signal probability graph generated after segmentation processing through a threshold value to obtain a white matter high-signal extraction graph, and superposing the white matter high-signal extraction graph on the standardized original image to obtain a white matter high-signal positioning graph. The white matter high signal extraction method is easy to operate, high in generalization, stable in effect, high in practicability and very effective.

Description

technical field [0001] The invention belongs to the technical field of medical imaging, and in particular relates to a white matter hyperintensity extraction method and system based on a lesion diffusion algorithm. Background technique [0002] White matter hyperintensities are usually managed using imaging modalities, most often visualized using a fluid-attenuated inversion recovery sequence (FLAIR) in magnetic resonance imaging. In the FLAIR sequence, the cerebrospinal fluid is suppressed as low signal, and the contrast of high signal lesions is enhanced. The imaging effect of white matter hyperintensity such as ventricles, ventricles and deep parts is better than other imaging methods. [0003] At present, the evaluation of white matter hyperintensities can be divided into manual evaluation and automatic evaluation. Manual evaluation refers to the naked-eye observation of MRI images by specialized personnel, and then uses Fazekas and other white matter lesion scales to e...

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Problems solved by technology

These methods usually take a long time to implement, have high requirements on the modality of the original image, and usually work best for specific samples, with insufficient generalization

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