Partition method for multiple-sclerosis damage area

Abstract

The invention discloses a partition method for a multiple-sclerosis damage area. The method comprises the following steps that preprocessing is carried out on a multi-sequence nuclear magnetic resonance image; manually marked training samples are learnt through a multinomial logistic regression method; classification is carried out on all pixels in the image after the preprocessing according to learnt parameters so that the possibility that all the pixels belong to different tissues can be obtained; an objective function is established through cooperation of classification probability and total variation regular terms, and the intermediate partition result of the multiple-sclerosis damage area is obtained by minimizing the objective function through a continuous max-flow algorithm; final damage area partitioning results are obtained by removing false positive areas according to the space distribution situations of the multiple-sclerosis damage. The partition method can improve the partition precision of the multiple-sclerosis damage area, and can be applied to effective detection of lesion areas in a brain nuclear magnetic resonance image.

Description

technical field [0001] The invention belongs to the field of medical image processing, in particular to the multiple sclerosis lesion region segmentation of MR sequence images. Background technique [0002] Multiple sclerosis is an inflammatory and demyelinating disease of the central nervous system and immune system. Nerve fibers, neurons, and oligospine cells are also damaged by the disease. To date, the cause of multiple sclerosis lesions is still unknown, but it is presumed to be influenced by some predisposing factors that cause the onset of the disease. To study this lesion, magnetic resonance imaging (MRI) is recognized as the optimal imaging method for detecting and studying multiple sclerosis lesions because of its high image resolution and contrast between soft tissues and other parts. Segmentation of brain MRI images has become a very important link in the clinical diagnosis and analysis of multiple sclerosis injuries. But radiologists manually segmenting the m...

AI Technical Summary

Problems solved by technology

However, for MRI images, due to the noise in the acquisition process, the offset field, and the differences between individual patients, there are large differences between individual images.

Therefore, only relying on the membership degree of a single voxel to determine the segmentation result will lead to not being robust to noise and offset fields, which will affect the final segmentation accuracy.

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